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IEEE Transactions on Geoscience and Remote Sensing
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 217  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892 - ISSN (Online) 1558-0644
Published by IEEE Homepage  [228 journals]
  • Bistatic Radar Tomography of Shear Margins: Simulated Temperature and
           Basal Material Inversions

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      Authors: Nicole Bienert;Dustin M. Schroeder;Paul Summers;
      Pages: 1 - 16
      Abstract: A significant portion of the sea level contributions of Antarctica and Greenland comes from ice streams, but the physical processes controlling ice stream width are poorly understood, especially when topographic controls are absent. Recent modeling studies have indicated that ice stream width may be controlled by elevated temperatures inside ice stream shear margins. While radio echo sounders can provide measurements of englacial water storage and subglacial conditions, existing radar-sounding techniques cannot measure temperature profiles at the scale required to test this hypothesis. We propose using a wide-angle radar survey and tomographic inversion to resolve temperature profiles, gradients, and anomalies at the scale required to study the thermophysical controls on shear margins. Recent work produced a bistatic radar system capable of obtaining the long offsets required for well-constrained inversions; however, shear-margin-specific temperature inversion techniques have not been developed for this system. In this article, we develop Newton’s method and alternating direction method of multipliers’ inversions for estimating temperature distribution and basal material across ice stream shear margins. We evaluate the performance of these inversion techniques on simulated bistatic radar-sounding data. Our results suggest that bistatic radar tomography experiments should be able to produce temperature maps on 50 m $times50$ m grids with $0.83~^{circ} text{C}~pm ~0.084~^{circ} text{C}$ mean temperature error, $3.58~^{circ} text{C}~pm ~0.20~^{circ} text{C}$ maximum temperature error, and an error in relative basal permittivity of $0.63~pm ~0.08$ for a 4-km transect.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • In-Orbit Geometric Calibration for Long-Linear-Array and Wide-Swath
           Whisk-Broom TIS of SDGSAT-1

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      Authors: Xiaoyan Li;Liyuan Li;Lixing Zhao;Jingjie Jiao;Linyi Jiang;Lan Yang;Fansheng Chen;Shengli Sun;
      Pages: 1 - 14
      Abstract: Because of the imaging mechanism complexity of long-linear-array and wide-swath whisk-broom thermal infrared spectrometer (TIS) of the first Sustainable Development Goals Satellite (SDGSAT-1), how to achieve a high geometric positioning accuracy (GPA) becomes the core factor in subsequent geometric quantitative applications. Here, in this article, a three-step in-orbit geometric calibration (GC) strategy comprising the estimations of exterior orientation parameters (EOPs), interior orientation parameters (IOPs), and scanning compensation parameters (SCPs) is proposed to correct the geo-location displacements for whisk-broom TIS. First, in accordance with the optical-mechanical structure and pinhole imaging theory, we establish the rigorous geometric positioning model (RGPM) of TIS and analyze the error resources term-by-term along the error propagation link elaborately. Second, the corresponding rigorous geometric calibration model (RGCM) is constructed in detail based on the 2-D look-angle model and the generalized bias correction matrix. Especially for eliminating the systematic nonlinear errors in the scanning direction, a fifth-degree polynomial is put forward to be employed to fit and compensate for the angular measurement errors of the scanning mirror. Finally, a three-step estimation method is presented to estimate the calibration parameters with ground control points (GCPs). Experimental results based on the spatial references of Landsat 8 panchromatic images and version 2 of advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM2) show that the GPA of the proposed method in along-track and cross-track directions can be better than 1.0 pixels for all three bands, which makes a great sense for associated geometric measurements.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • TriHex: Combining Formation Flying, General Circular Orbits, and
           Alias-Free Imaging, for High-Resolution L-Band Aperture Synthesis

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      Authors: Manuel Martín-Neira;Francesca Scala;Alberto M. Zurita;Martin Suess;Miguel Piera;Berthyl J. Duesmann;Matthias Drusch;Camilla Colombo;Don De Wilde;Josep Closa Soteras;Erio Gandini;Raúl Díez-García;Roger Oliva;Ignasi Corbella;
      Pages: 1 - 17
      Abstract: The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), together with NASA’s Soil Moisture Active Passive (SMAP) mission, is providing a wealth of information to the user community for a wide range of applications. Although both missions are still operational, they have significantly exceeded their design life time. For this reason, ESA is looking at future mission concepts, which would adequately address the requirements of the passive L-band community beyond SMOS and SMAP. This article proposes one mission concept, TriHex, which has been found capable of achieving high spatial resolution, radiometric resolution, and accuracy, approaching the user needs. This is possible by the combination of aperture synthesis, formation flying, the use of general circular orbits, and alias-free imaging.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Application of FY-4B Geostationary Meteorological Satellite in Grassland
           Fire Dynamic Monitoring

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      Authors: Jie Chen;Ying Wu;Shuang Wu;Lianni Xie;Jun Tang;Zuomin Xu;Xiuzhen Han;Xiaomin Ma;Wei Zheng;Tao Sun;Cheng Liu;
      Pages: 1 - 9
      Abstract: In this study, the channel data related to fire point identification of Advanced Geostationary Radiation Imager on Fengyun-4B (FY-4B/AGRI) and Advanced Meteorological Imager on GEO-KOMPSAT 2A (GK-2A/AMI) were cross-compared. A total of 267 sampling points in China (Guangdong, Guangxi, Guizhou, Yunnan, and Hainan) were selected to carry out fine positioning correction on the data in different elevation intervals. Then, a fire monitoring algorithm based on Fengyun-4B (FY-4B) was proposed, in which the self-adaptive threshold adjustment of the underlying surface parameters and the reprocessing module of fire point identification were added. The algorithm can realize the high-precision and stable monitoring of fire. The continuous dynamic monitoring was carried out using the grassland fire Mongolia from April 18 to 20, 2022 as an example. The results showed that the parameters of FY-4B/AGRI and GK2A/AMI channels have high consistency. The root-mean-square error (RMSE) of reflection channel was 1.33%, and the maximum RMSE of brightness temperature channel was less than 1.3 Kelvin (K). Through the positioning analysis in different elevation intervals, the average longitude offset of FY-4B satellite data was -0.5° to 0° and the average latitude offset was -0.9° o 0.6°. Overall, these findings indicate that the high-frequency observations of FY-4B can be fully utilized to monitor forest and grassland fires, which can continuously track the dynamic evolution of fire and can distinguish the spatial distribution of different fire intensities in large-scale fire fields.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • IR Digital Holography for Remote Sensing of Structures

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      Authors: Eugenio Pugliese;Massimiliano Locatelli;Flavio Bocchi;Pasquale Poggi;David Jafrancesco;Giuseppe Falzone;Nicola Signorini;Daniele Spina;Riccardo Meucci;
      Pages: 1 - 7
      Abstract: In this article, we present a comparison between the data obtained by infrared digital holography (IRDH) and conventional accelerometers in order to evaluate the structural stability of buildings. The tests have carried out on historical buildings located in Tuscany (Italy) under the continuous surveillance of the Italian Civil Protection Department (DPC) by means of accelerometers networks. From the comparison, it emerges that the holographic technique can be profitably used in the field of dynamical analysis of structures thanks to its high sensitivity and to the possibility to operate from remote. Indeed, the holographic technique is able to detect micromovements of the order of $mathbf {1/100}$ of the used laser wavelength at $mathbf {10.6 ~mu {mathrm{ m}}}$ without entering the structures.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • 2-D Modeling and Analysis of Time-Domain Electromagnetic Anomalous
           Diffusion With Space-Fractional Derivative

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      Authors: Yibing Yu;Quanming Gao;Xuejiao Zhao;Yanju Ji;
      Pages: 1 - 13
      Abstract: Recently, the electromagnetic (EM) anomalous diffusion phenomenon has been observed in time-domain EM (TDEM) surveys. Furthermore, the data interpretation accuracy has been reduced by adopting the traditional EM theory and methods. A number of models, such as random medium and roughness electrical conductivity theory, have been adopted to model the EM anomalous diffusion. However, problems such as modeling difficulty and massive discretization exist regarding characterizing the long-range correlation of EM anomalous diffusion. The space-fractional derivative has been proven to preferably describe the long-range correlation characteristic. Only a handful of studies on TDEM anomalous diffusion with space-fractional derivative have been conducted due to the difficulties in computational engineering problems. Therefore, we performed a series of studies about 2-D TDEM anomalous diffusion with space-fractional derivative. The 2-D TDEM space-fractional diffusion equation was constructed based on the space-fractional Ohm’s law model. Furthermore, the discretization and iteration forms of the control equation were derived based on the finite element method (FEM) by introducing the Riemann–Liouville (R–L)-type Riesz fractional derivatives. The 2-D mountain-shaped function and partial integration method (PIM) were combined to convert the fractional derivative into the primitive function form. Hence, the 2-D modeling of the space-fractional EM diffusion was realized. The effectiveness of our method was verified by the function construction method and wavenumber-domain analytical solution. The spatial and temporal characteristics of the space-fractional EM diffusion were analyzed by different geological models. Furthermore, we discuss the differences with the classical EM diffusion. Our method can effectively model the space-fractional EM diffusion in TDEM surveys and provide theoretical bases for improving the TDEM inter-retation accuracy with complex geological conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Method for Separating the O/X Mode Signals in Vertical Ionograms Based
           on Improved U-Shaped Encoder–Decoder Network

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      Authors: Hongchun Li;Chengfeng Zhang;Bo Yin;Xiaoyi Jia;Jiali Xu;Jiawei Xue;Mengfei Ma;
      Pages: 1 - 13
      Abstract: The accuracy of O/X mode separation in vertical ionograms directly determines the quality of pattern discrimination results and metrics, which is of great significance to ionospheric research. It is extremely complicated to separate the O/X mode of the vertical ionograms because of the environmental noise, interference, and the time-varying dispersion characteristics of the ionosphere itself. In this article, we propose a method for separating the O/X mode signal in vertical ionograms based on an improved U-shaped encoder–decoder network, named vertical ionogram separation U-shaped network (VIS-UNet). Our model is based on the encoder–decoder architecture. It introduces the residual convolution to avoid network performance degradation and utilizes the attention module to improve the attention of the signal characteristic. In addition, we design an adaptive loss function to expedite the convergent speed of the model. Experimental results show that our model performs better than the baselines for the task of the O/X mode signal separation: 1) the method in this article has low requirements on the vertical ionospheric sounding system and the ionograms obtained by the single-channel vertical ionospheric sounding system can realize the separation of O/X mode signal at the pixel level; 2) it has strong universality and is insensitive to the signal integrity and the ionospheric pattern of the vertical ionograms; and 3) it performs better for the separation task. The mean intersection over union (MIOU) of the O/X mode separation task reaches 91.97% and the performance is significantly improved compared with the existing methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Asymmetric Cross-Attention Hierarchical Network Based on CNN and
           Transformer for Bitemporal Remote Sensing Images Change Detection

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      Authors: Xiaofeng Zhang;Shuli Cheng;Liejun Wang;Haojin Li;
      Pages: 1 - 15
      Abstract: As an important task in the field of remote sensing (RS) image processing, RS image change detection (CD) has made significant advances through the use of convolutional neural networks (CNNs). The transformer has recently been introduced into the field of CD due to its excellent global perception capabilities. Some works have attempted to combine CNN and transformer to jointly harvest local-global features; however, these works have not paid much attention to the interaction between the features extracted by both. Also, the use of the transformer has resulted in significant resource consumption. In this article, we propose the Asymmetric Cross-attention Hierarchical Network (ACAHNet) by combining CNN and transformer in a series-parallel manner. The proposed Asymmetric Multiheaded Cross Attention (AMCA) module reduces the quadratic computational complexity of the transformer to linear, and the module enhances the interaction between features extracted from the CNN and the transformer. Different from the early and late fusion strategies employed in previous work, the effectiveness of the mid-term fusion strategy employed by ACAHNet shows a new choice of timing for feature fusion in the CD task. Our experiments on the proposed method on three public datasets show that our network has a better performance in terms of effectiveness and computational resource consumption compared to other comparative methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiple-Space Deep Learning Schemes for Inverse Scattering Problems

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      Authors: Yusong Wang;Zheng Zong;Siyuan He;Zhun Wei;
      Pages: 1 - 11
      Abstract: Recently, deep learning methods have made significant success in inverse scattering problems (ISPs). However, learning approaches that work in different spaces, such as frequency and real space, are seldom explored in solving ISPs. In this work, multiple-space deep learning schemes (MSDLSs) incorporating frequency-space and real-space processing are studied. Specifically, a network that works in low-frequency subspace is first introduced. Then, serial MSDLSs are introduced by combining frequency-space and real-space networks in a serial way to enable networks in different spaces work complementarily. Finally, to further enable dynamic interaction between multiple-space information during both training and testing stages, a parallel MSDLS is proposed. The proposed MSDLSs are presented under the framework of backpropagation scheme (BPS). It is shown by synthetic and experimental tests that the MSDLSs have a consistent improvement over BPS. It is expected that the proposed schemes will find applications on other inverse problems where an incorporation of multiple-space information is needed.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Three-Dimensional Ray-Based Tomographic Approach for Contactless GPR
           Imaging

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      Authors: Gianluca Gennarelli;Carlo Noviello;Giovanni Ludeno;Giuseppe Esposito;Francesco Soldovieri;Ilaria Catapano;
      Pages: 1 - 14
      Abstract: This article proposes a 3-D imaging approach for contactless ground penetrating radar surveys. The imaging problem is formulated in the linear inverse scattering context and solved by using the singular values decomposition tool. A ray-based model accounting for the electromagnetic (EM) signal propagation into an inhomogeneous medium is developed to accurately evaluate the kernel of the integral equation to be inverted. Under the proposed model, an analysis of the spatial resolution performance is carried out as a function of the geometrical and EM parameters of the scenario. To this end, theoretical concepts based on diffraction tomography and the singular value decomposition of the scattering operator are exploited. Reconstruction results based on full-wave simulated data assess the feasibility of the imaging approach.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Comprehensive Emission Model for Layered Irregular and Inhomogeneous
           Medium

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      Authors: Dongjin Bai;Xiaolong Dong;Saibun Tjuatja;Di Zhu;Zijin Zhang;
      Pages: 1 - 20
      Abstract: Reported radiometric measurements indicate that incoherent and coherent radiative transfer processes characterize the microwave emission features of layered irregular and inhomogeneous medium. In order to develop a forward emission model to be capable of considering the medium and boundary scattering and coherent boundary interaction that may exist in general layered medium, in this article, we present a comprehensive layer emission model (CLEM) based on the scattering operator formulation. We introduce wave-based coherent multiple reflection operators to account for coherent boundary interaction and first integrate them with the intensity-based multiple scattering processes, allowing the comprehensive description of rough boundary scattering, volume scattering, medium/boundary interaction, and coherent boundary interaction in the framework of CLEM. Simulations and analyses for ice- and snow-covered ground cases are conducted based on CLEM to evaluate the coherent boundary interaction and different impacting factors. Validation on CLEM is conducted with emission observations of snow-covered terrain in campaign Nordic Snow Radar Experiment (NoSREx) 2010–2013 during dry snow period and time-series emission observations of frozen soil during freezing and thawing processes. CLEM simulation results show a good agreement with measurements. For NoSREx, root-mean-square errors (RMSEs) at L- to Ka-band are below 5.5 K for both polarizations, which are of different levels of promotion compared with the incoherent model simulations, especially for horizontal polarization at L- and X-band. Application to frozen soil case illustrates the capability of CLEM to explain coherent oscillation feature with impact from incoherent scattering effect.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • New Seawater Dielectric Constant Parametrization and Application to SMOS
           Retrieved Salinity

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      Authors: Jacqueline Boutin;Jean-Luc Vergely;Fabrice Bonjean;Xavier Perrot;Yiwen Zhou;Emmanuel P. Dinnat;Roger H. Lang;David M. Le Vine;Roberto Sabia;
      Pages: 1 - 13
      Abstract: The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the reliability of the dielectric constant model. Two new parametrizations were recently developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements by Boutin et al. (2021) (BV), and on the other hand, on new George Washington University laboratory measurements by Zhou et al. (2021) (GW2020). These two approaches are fully independent. For most SSS and Sea Surface Temperature (SST) conditions commonly observed over the open ocean, the relative variations of brightness temperatures Tb simulated through the BV and GW2020 parametrizations agree particularly well, and better than with earlier parametrizations previously used in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. Nevertheless, uncertainty remains, especially below 10 °C where a $sim $ 0.1 K relative difference between the two models is observed. This motivates the development of a revised parameterization, BVZ, based on a methodology similar to that used to derive BV but using GW2020 instead of SMOS measurements. Compared to the GW2020 parameterization, BVZ is derived with a reduced number of degrees of freedom, it relies on the TEOS10 PSS78 conductivity-salinity relationship, and on the previously derived static permittivity of fresh water. One month per season of SMOS data have been reprocessed in 2018 using BV, GW2020, and BVZ. We find the best overall agreement between SMOS SSS and Argo SSS with BVZ parametrization, with noticeable improvement in the 5 °C–15 °C SST range.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • E2DTF: An End-to-End Detection and Tracking Framework for Multiple
           Micro-UAVs With FMCW-MIMO Radar

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      Authors: Xin Fang;Jing Zhu;Darong Huang;Zhenyuan Zhang;Guoqing Xiao;
      Pages: 1 - 16
      Abstract: Due to the weak radar echoes and strong background clutters in low-altitude airspace, the detection and tracking of multiple micro-unmanned aerial vehicles (UAVs) have posed formidable challenges in the radar surveillance field. Consequently, this article proposes an end-to-end detection and tracking framework ( $text{E}boldsymbol{ {^{2}}}$ DTF) for multiple micro-UAVs by utilizing the frequency-modulated continuous-wave-multiple-input–multiple-output (FMCW-MIMO) radar. To address the low signal-to-noise ratio (SNR) problem, $text{E}boldsymbol{ {^{2}}}$ DTF presents a frame-range-Doppler-azimuth information fusion filter to integrate the target energy by exploiting the spatiotemporal dependence of positions within a sequence of unthresholded frames. In addition, considering that a target may enter/leave the radar field-of-view (FOV), $text{E}boldsymbol{ {^{2}}}$ DTF introduces a target model state, updated by an extended Markov state transition matrix sequentially, to realize an unknown, time-varying number of micro-UAVs tracking. Another nice feature of $text{E}boldsymbol{ {^{2}}}$ DTF is that it avoids the complex data association procedure thanks to removing the threshold-decision operation. Finally, both numerical simulations and experiments with five different scenarios, i.e., horizontal line, cross-trajectory, circular loop, rainy condition, and 3-D trajectory tracking, are presented to verify the effectiveness of the proposed method. The results show that $text{E}boldsymbol{ {^{2}}}$ DTF can obtain superior detection and tracking performance for multiple micro-UAVs in contrast -o the state-of-the-art methods considering detection and tracking processes independently, especially under low SNR conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Domain Adaptive Remote Sensing Scene Recognition via Semantic Relationship
           Knowledge Transfer

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      Authors: Ying Zhao;Shuang Li;Chi Harold Liu;Yuqi Han;Hao Shi;Wei Li;
      Pages: 1 - 13
      Abstract: Scene recognition has attracted rising attentions of many researchers in the remote sensing fields, owing to the rapidly advancing of remote sensing devices in recent years. However, images obtained from various sensors dominate diverse sensor-specific characteristics, which will dramatically weaken the model transferability trained on a source data domain to a different target domain on account of the domain shift issues. To mitigate the domain discrepancy, most existing methods attend to align the cross-domain distributions. While the valuable knowledge of semantic relationships between different scenes is generally overlooked, and the underlying correlation across scenes cannot be fully discovered. For the sake of tackling this challenge, we propose an adaptive remote sensing scene recognition network, which can successfully transfer both the discriminative knowledge and cross-scene relationship from source to target. Specifically, in this article, we acquire sensor-invariant representations in an adversarial manner and realize fine-grained conditional distribution alignment contrastively. In such a way, the tremendous domain gap can be mitigated to a large extent, and the discriminative and well-matched representations will be derived favorably. In addition, we explicitly construct classwise relationship distributions belonging to two domains, respectively, and minimize their divergence to conduct semantic relationship knowledge transfer (SRKT), for the purpose of sufficiently unearthing the intrinsic semantic relative structures that can prompt generality of the model in the target domain. Finally, we conduct multiple experiments on representative multidomain remote sensing benchmarks, and the extensive experimental results demonstrate the superiority of our proposed approach.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Statistical Correlation Between DEMETER Satellite Electronic Perturbations
           and Global Earthquakes With M ≥ 4.8

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      Authors: Ying Zhang;Mei Li;Qinghua Huang;Zhigang Shao;Jing Liu;Xuemin Zhang;Weiyu Ma;Michel Parrot;
      Pages: 1 - 18
      Abstract: Upon obtaining a relatively low false discovery rate (FDR) of alarms and a low false negative rate (FNR) of earthquakes, several previous long-term statistical researches concluded that ionospheric perturbations recorded by satellites are statistically related to earthquakes. However, overly large time–space windows for correlating perturbations with earthquakes will also contribute to low FDR and FNR. In this study, a new score—the number of non-randomly successful alarms—is used to quantitatively describe the sensitivity of electron density perturbations (EDPs) recorded by the Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER) satellite to global earthquakes with $M ge4.8$ . Results show that the EDPs are significantly related to global medium-to-strong earthquakes and that optimal parameters for removing EDPs which are non-related to earthquakes and the optimal time–space windows for correlating earthquakes and EDPs are variable in space. Moreover, our results show that the intensity of EDPs makes little contribution to distinguishing the perturbations related to earthquakes with different magnitudes and perturbations non-related to earthquakes, while the $K_{mathrm {p}}$ index is effective for improving the signal/noise ratio of our model, where signal/noise refers to the EDPs related/non-related to earthquakes. Finally, using the optimal time–space windows for correlating EDPs and earthquakes, we construct several earthquake prediction models and quantitatively evaluate their power. We find that these EDP-based earthquake predictions are better than the spatially variable Poisson model showing the great potential of predicting earthquakes based on satellite-based Earth observation techniq-es. However, the spatio-temporal accuracy of these models for predicting earthquakes is not satisfactory, as the alerted time–space volume is big.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Modeling and Analysis of Litz Wire Radio Frequency (RF) Coil in Inside-Out
           NMR Well Logging Sensor

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      Authors: Xianneng Xu;Zheng Xu;
      Pages: 1 - 9
      Abstract: Nuclear magnetic resonance (NMR) well logging is an important method of oil measurement, and the radio frequency (RF) coil is an important component for obtaining accurate parameters of rock formation. The use of Litz wire as RF coil can help minimize the resistance and increase the signal-to-noise ratio (SNR) of NMR signals. However, the electromagnetic field computing and performance evaluation of Litz wire are complicated and difficult. In this study, we proposed an effective model for analyzing the Litz wire in inside-out NMR logging sensor. Considering the electromagnetic parameters of the coil itself, the frequency response of excitation, and tuning and matching circuit, we proposed a new and accurate model for SNR analysis. We then introduced a homogenization-based finite-element method (FEM) to calculate the Litz wire around a ferrite core, and the electromagnetic parameters of Litz wire with different structures were obtained accurately and quickly. We compared the Litz wire and enameled wire through our proposed model. Results show that the SNR of Litz wire is remarkably higher than that of enameled wire, and the model analysis results are consistent with the measured results. Suggestions for further reducing the ac resistance of RF coil composed of Litz wire are provided.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Movable Object Detection in Remote Sensing Images via Dynamic Automatic
           Learning

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      Authors: Xiang Zhang;Hangzai Luo;Sheng Zhong;Lei Tang;Jinye Peng;Jianping Fan;
      Pages: 1 - 14
      Abstract: The performance of deep networks for object detection in remote sensing images (RSIs) largely depends on the availability of large-scale training images whose labels are given at the bounding-box level through a labor-intensive manual labeling process. To alleviate the huge burden of providing bounding-box annotations manually for movable objects, we propose a new approach, called dynamic automatic learning (DAL), to progressively learn object detectors. Specifically, a novel initial annotation generation (IAG) strategy is first designed to produce bounding-box annotations for movable objects in multitemporal RSIs. During this process, image-level labels need to be manually labeled for the generated candidates. Next, a detection network learns the detection knowledge from multitemporal RSIs with bounding-box annotations and then transfers the knowledge to generate pseudoboxes for the unlabeled data. Finally, with these pseudoboxes, the object detector can be optimized for generating accurate pseudoboxes iteratively. Furthermore, we introduce a pseudobox filtering (PBF) strategy to purify the quality of pseudoboxes to obtain accurate supervision. Our experiments on the challenging Northwestern Polytechnical University (NWPU) VHR-10.v2 and object detection in optical remote sensing images (DIOR) datasets have demonstrated that our DAL approach can achieve competitive results compared with state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Efficient Calculation of the Kirchhoff Integral for Predicting the
           Bistatic Normalized Radar Cross Section of Ocean-Like Surfaces

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      Authors: Joel T. Johnson;Jakov V. Toporkov;Paul A. Hwang;Jeffrey D. Ouellette;
      Pages: 1 - 14
      Abstract: Methods for improving efficiency in computing bistatic scattering from ocean-like surfaces are presented. The methods focus on approximations for the Kirchhoff integral required in evaluating the predictions of multiple theories of bistatic scattering from the ocean surface including the physical optics (PO) and small slope approximation (SSA) approaches. A representation of the integral in terms of the probability density function (pdf) of a symmetric alpha stable random variable is examined for near-specular scattering as has been proposed previously in the literature. Methods for evaluating this form are presented for azimuthally varying surfaces having a general spectrum model as well as a fully analytical expression for surfaces described by an isotropic Pierson–Moskowitz spectrum. The resulting forms are also compared and contrasted with the standard geometrical optics (GO) theory of surface scattering to provide insight into the cutoff wavenumber used in the standard GO method. Additional two-scale and small roughness approximations to the integral are also investigated for the prediction of scattered fields outside the near-specular region.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Optimization of Stream-Function-Based Coil Heads for EMI Systems

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      Authors: Mark A. Reed;Waymond R. Scott;
      Pages: 1 - 15
      Abstract: Many different coil head configurations are used in electromagnetic induction (EMI) systems for sensing buried targets, but these coils are most often designed by altering known winding configurations because a general wire parameterization is difficult to create and to optimize for target sensitivity. The soil sensitivity, which is important in mineralized soils, is also often not taken into account. This work presents a method of parameterizing wire coils using stream functions. A new set of normalized metrics for analyzing the coils as stream functions is demonstrated, and then the metrics and stream functions are optimized using a new biconvex optimization procedure. The stream functions are then converted back into wire coils to demonstrate the efficacy of the stream-function parameterization. Results show an improvement in target and soil sensitivity performance over known conventional wire coil configurations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Ice Cloud Retrieval Algorithm for Submillimeter Wave Radiometers:
           Simulations and Application to an Airborne Experiment

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      Authors: Pingyi Dong;Lei Liu;Shulei Li;Letu Husi;Shuai Hu;Lingbing Bu;
      Pages: 1 - 9
      Abstract: A retrieval methodology based on the Bayesian neural network (BNN) is presented that inverts the ice water path (IWP), mean mass-weighted diameter ( $D_{mathrm {me}}$ ), and cloud height of ice clouds from submillimeter radiometer observations. The training dataset was created using collecting cloud profiles from the DARDAR (raDAR/liDAR) database and running simulations by the atmospheric radiative transfer simulator (ARTS) model. Since the effective radius ( $r_{e}$ ) is the size descriptor of ice particles in the DARDAR database, a lookup table of ice water content (IWC), $D_{mathrm {me}}$ , and $r_{e}$ was constructed to convert $r_{e}$ profiles into $D_{mathrm {me}}$ profiles. In addition, random noises corresponding to the measurement uncertainties of the compact scanning submillimeter-wave imaging radiometer (CoSSIR) during the TC4 experiment were added to the simulated brightness temperatures before training the BNN. The proposed retrieval method was first applied to the simulated testing database and then to the observations of CoSSIR. Moreover, the retrieved IWP and $D_{mathrm {me}}$ were compared to the retrievals of the Bayesian Monte Carlo integration (BMCI) method. The retrieved cloud height was assessed by cloud height extracted from the reflectivity data of the cloud radar system (CPS) flow on the same aircraft with CoSSIR. The comparison showed that the correlation coefficients of the retrieved IWP and $D_-mathrm {me}}$ from the two methods are above 0.8, and the retrieved cloud height also showed good agreement with that extracted from the CPS.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Discrete Wavelet Transform-Based Gaussian Mixture Model for Remote Sensing
           Image Compression

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      Authors: Shao Xiang;Qiaokang Liang;Leyuan Fang;
      Pages: 1 - 12
      Abstract: High-ratio image compression is difficult because remote sensing images have complex backgrounds and rich information, and the correlation between features is weak. An accurate entropy model is an important way to solve the problem by enhancing the representation ability of the compression models. The entropy model is more suited to estimate the probability distributions with the sparse latent representations. This study proposes a novel entropy model [discrete wavelet transform Gaussian mixture model (DWTGMM)] based on discrete wavelet transform (DWT) and Gaussian mixture model (GMM) for remote sensing image compression. The method uses DWT to transform the latent representations into a wavelet domain and obtains four sparse representations, and then uses the proposed DWTGMM to model them separately to estimate the probability distribution of each element. It is noteworthy that the DWT used in our approach does not require learning parameters and can be combined with other entropy models to acquire the distribution of latent representations. To evaluate our method, we construct three remote sensing image datasets, i.e., GoogleMap, GF1, and GF7. We compare our method with several popular learned compression models and traditional codecs. Experimental results show that the proposed method can achieve excellent performance with low complexity. Especially with the same model architecture, the DWTGMM achieves the best compression performance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Spatiotemporal Interpolation Graph Convolutional Network for Estimating
           PM₂.₅ Concentrations Based on Urban Functional Zones

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      Authors: Xinya Chen;Yinghua Zhang;Yuebin Wang;Liqiang Zhang;Zhiyu Yi;Hanchao Zhang;P. Takis Mathiopoulos;
      Pages: 1 - 14
      Abstract: Urban functional zones (UFZs) contain abundant landscape information that can be adopted to better understand the surroundings. Various landscape compositions and configurations reflect different human activities, which may affect the particulate matter (PM2.5) concentrations. The very high-resolution (VHR) image features can reflect the physical and spatial structures of the UFZs. However, the existing PM2.5 estimation methods neither have been based on the scale of UFZs, nor have the VHR image features of UFZs as independent variables. Hence, this article proposes a spatiotemporal interpolation graph convolutional network (STI-GCN) model and introduces VHR image features to achieve PM2.5 estimation in UFZs. First, UFZs are split, and VHR image features are extracted by the visual geometry group 16 (VGG16). Subsequently, meteorological factors, aerosol optical depth (AOD), and VHR image features are used to estimate the PM2.5 concentrations at the scale of the UFZs. The two metropolises, Beijing and Shanghai, are chosen to assess the validity of the STI-GCN model. As for Beijing and Shanghai, the overall accuracy ${R^{2}}$ of the STI-GCN model can reach 0.96 and 0.89, the root-mean-square errors (RMSEs) are 8.15 and 6.40 $mu text {g}/{text {m}^{3}}$ , the mean absolute errors (MAEs) are 5.51 and 4.78 $mu text {g}/{text {m}^{3}}$ , and the relative prediction errors (RPEs) are 18.53% and 17.38%, respectively. Experiments show that the STI-GCN consistently outperforms other models. What’s more, the PM2.5 values are relatively high in commercial and official zones (COZs) and relatively low in urban green zones (UGZs).
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Absorbing Aerosol Optical Depth From OMI/TROPOMI Based on the GBRT
           Algorithm and AERONET Data in Asia

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      Authors: Ding Li;Jason Blake Cohen;Kai Qin;Yong Xue;Lanlan Rao;
      Pages: 1 - 10
      Abstract: Quantifying the concentration of absorbing aerosol is essential for pollution tracking and calculation of atmospheric radiative forcing. To quickly obtain absorbing aerosol optical depth (AAOD) with high-resolution and high-accuracy, the gradient boosted regression trees (GBRT) method based on the joint data from Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectro-Radiometer (MODIS), and AErosol RObotic NETwork (AERONET) is used for TROPOspheric Monitoring Instrument (TROPOMI). Compared with the ground-based data, the correlation coefficient of the results is greater than 0.6 and the difference is generally within ±0.04. Compared with OMI data, the underestimation has been greatly improved. By further restricting the impact factors, three valid conclusions can be drawn: 1) the model with more spatial difference information achieves better results than the model with more temporal difference information; 2) the training dataset with a high cloud fraction (0.1–0.4) can partly improve the performance of GBRT results; and 3) when aerosol optical depth (AOD) is less than 0.3, the perform of retrieved AAODs is still good by comparing with ground-based measurements. The novel finding is expected to contribute to regional and even urban anthropogenic pollution research.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Relative Importance of Radar Variables for Nowcasting Heavy Rainfall: A
           Machine Learning Approach

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      Authors: Yi Victor Wang;Seung Hee Kim;Geunsu Lyu;Choeng-Lyong Lee;Gyuwon Lee;Ki-Hong Min;Menas C. Kafatos;
      Pages: 1 - 14
      Abstract: Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction (NWP) models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell (CC), mesoscale CC, diagonal squall line (SLD), and parallel squall line (SLP), was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking (FAST), over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via five ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Toward a Globally-Applicable Uncertainty Quantification Framework for
           Satellite Multisensor Precipitation Products Based on GPM DPR

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      Authors: Zhe Li;Daniel B. Wright;Samantha H. Hartke;Dalia B. Kirschbaum;Sana Khan;Viviana Maggioni;Pierre-Emmanuel Kirstetter;
      Pages: 1 - 15
      Abstract: The usefulness of satellite multisensor precipitation products such as NASA’s 30-min, 0.1° Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) is hindered by their associated errors. Reliable estimates of uncertainty would mitigate this limitation, especially in near-real time when gauge observations are not available. However, creating such estimates is challenging, due to both the complicated nature of satellite precipitation errors and the lack of “ground-truth” data precisely in the places—including oceans, complex terrain, and developing countries—that could benefit most from satellite precipitation estimates. In this work, we use the GPM dual-frequency precipitation radar (DPR)-derived swath-based precipitation products as an alternative to ground-based observations to facilitate IMERG uncertainty estimation. We compare the suitability of two DPR-derived precipitation products, 2ADPR and 2BCMB, against higher fidelity ground validation multiradar multisensor (GV-MRMS) ground reference data over the contiguous United States. The 2BCMB is selected to train error models based on censored shifted gamma distribution (CSGD; a mixed discrete-continuous probability distribution). Uncertainty estimates from these models are compared against alternative error models trained on GV-MRMS. Using information from NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis, we also demonstrate how IMERG uncertainty estimates can be further constrained using additional precipitation-related predictors. Though several critical issues remain unresolved, the proposed method shows promise for yielding robust uncertainty estimates in near-real time for IMERG and other similar precipitation products at their native resolution across the entire globe.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Machine-Learned Cloud Classes From Satellite Data for Process-Oriented
           Climate Model Evaluation

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      Authors: Arndt Kaps;Axel Lauer;Gustau Camps-Valls;Pierre Gentine;Luis Gómez-Chova;Veronika Eyring;
      Pages: 1 - 15
      Abstract: Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change projections. This study introduces a new machine-learning-based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument labeled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud-type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud-type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labeled satellite data for a more systematic evaluation of clouds in climate models.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Spectral Energy Model-Driven Inversion of XCO2 in IPDA Lidar
           Remote Sensing

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      Authors: Haowei Zhang;Ge Han;Xin Ma;Siwei Li;Hao Xu;Tianqi Shi;Jianye Yuan;Wanqin Zhong;Yanran Peng;Jingjing Xu;Wei Gong;
      Pages: 1 - 9
      Abstract: Carbon observation satellites based on passive theory (e.g., OCO-2/3, GOSAT-1/2, and TanSat) have relatively high carbon dioxide column concentration (XCO2) accuracy when the observation conditions are met. Passive satellites have data bias and coverage deficiencies due to cloud cover, low albedo, low-light conditions, and aerosol scattering, resulting in carbon observation satellites based on passive theory that cannot meet the demand for high-precision, all-day, all-weather XCO2 monitoring. Active detection satellites are urgently needed to support global carbon sources, sinks, and carbon neutrality. China intends to launch a sensor satellite with active detection of XCO2 in the coming years. In this work, based on the satellite’s scaled-down airborne experiments, a spectral energy model was developed to optimize the conventional inversion algorithm and achieve a more accurate XCO2 inversion. The 1.572- $mu text{m}$ integrated path differential absorption (IPDA) lidar column length is used indirectly to evaluate the accuracy of the spectral energy model for signal extraction. Also, the experimental results show that the accuracy of the signal extracted by the 1.572- $mu text{m}$ IPDA lidar column length is 0.74 and 6.20 m at sea and on land based on the indirect evaluation of the length of the 1.572- $mu text{m}$ IPDA lidar column length. The optimized XCO2 was evaluated (standard deviation as an evaluation metric) and its XCO2 standard deviation reduced by 31%, 63%, and 66% in the ocean, plains, and mountains, respectively. Our algorithm can obtain the XCO2 with a consistent trend by using XCO2 from the OCO-2 satellite as a reference. The calculated XCO2 is more accurat- in areas dominated by anthropogenic factors (plains), due to the accuracy of the IPDA detection mechanism. This algorithm improves the accuracy and robustness of XCO2 inversion and has important reference significance for the IPDA lidar carried by China’s satellites to be launched in this year.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Tomographic Reconstruction of Water Vapor Density Fields From the
           Integration of GNSS Observations and Fengyun-4A Products

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      Authors: Biyan Chen;Jingshu Tan;Wei Wang;Wujiao Dai;Minsi Ao;Chunhua Chen;
      Pages: 1 - 12
      Abstract: The potential of precipitable water vapor (PWV) maps retrieved by remote sensing satellites can address the geometry defect of global navigation satellite system (GNSS) observations in tropospheric tomography. The second-generation geostationary meteorological satellite Fengyun-4A (FY-4A) of China can provide PWV products with high spatial (4 km) and temporal (15 min) resolutions. This article presents the first study on water vapor tomography by integrating GNSS measurements and FY-4A products using the node-based parameterized method. Layer PWV (LPW) products of FY-4A instead of the total PWV are adopted, which can increase the rank of the tomographic equation significantly. The integrated tomography model is validated with observational data collected over the three-month period of June to August 2020 from 124 GNSS stations in Hunan province, China. Assessments using radiosonde and European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) data demonstrate the better performance of the integrated model against the traditional model using GNSS data alone. In the assessment with radiosonde profiles, the integrated model improves the tomographic solutions upon the traditional model by 40.58% and 36.33% for 30- and 15-min resolutions, respectively. Root mean square errors (RMSEs) of density differences between ERA5 and the integrated model vary from 1.24 to 2.82 g/ $text{m}^{3}$ throughout the study area. RMSEs vertically decrease from $sim 5$ g/ $text{m}^{3}$ at the bottom to $sim 0.5$ g/ $text{m}^{3}$ at the top layer of about 10 km. This work demonstrate- the benefit of high-quality FY-4A products to GNSS tomography because they can effectively mitigate the ill-posed problem of inverse process.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Improved Aerosol Lidar Ratio Profile by Introducing Pseudo-Constant

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      Authors: Hongzhu Ji;Siying Chen;Pan Guo;Jingxi He;Yuefeng Zhao;Xinye Fan;
      Pages: 1 - 10
      Abstract: Aerosol lidar ratio (LR) is important for the inversion of aerosol optical characteristics. Single aerosol LR value adopted in the inversion not only causes difficulties in improving aerosol inversion accuracy but also hinders the analysis of aerosol types. In this article, a pseudo-constant method with certain universality is proposed for inverting aerosol LR profiles. Simulation results show a decrease in the mean absolute error (MAE) of the aerosol LR from 3.57% (iteration method) and 9.96% (Fernald method) to 1.82% (pseudo-constant method). In the near-surface region (< 1.5 km), the MAE of aerosol LR decreases from 4.97% (iteration) and 21.66% (Fernald) to 1.54% (pseudo-constant). Moreover, the inversion accuracy of aerosol LR obtained from pseudo-constant method is less influenced by the precision of the pre-estimated aerosol LR value than that from iteration method. Experimentally, the aerosol LR and aerosol extinction coefficient (AEC) profiles obtained from the pseudo-constant method are more accurate than those from the iteration and Fernald methods. In addition, both the simulations and experiments indicate that the pseudo-constant method has higher efficiency; the processing time is shortened from approximately 5 s/signal (iteration) to approximately 0.1 s/signal. Finally, aerosol types can be distinguished to a certain degree by combining the improved aerosol LR profile.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Fast Piecewise-Defined Neural Network Method to Retrieve Temperature and
           Humidity Profile for the Vertical Atmospheric Sounding System of
           FengYun-3E Satellite

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      Authors: Wenguang Bai;Peng Zhang;Hui Liu;Wenjian Zhang;Chengli Qi;Gang Ma;Guannan Li;
      Pages: 1 - 10
      Abstract: A fast piecewise-defined neural network (PDNN) method is presented to produce accurate atmospheric temperature and humidity profiles from satellite hyperspectral infrared (IR) and microwave (MW) observations in all-sky conditions. The PDNN method relies on a novel classification approach and a principal component-based neural network (NN) function to better capture the nonlinear relationship between the spectral radiances and atmospheric state vectors. The algorithm was designed to only rely on satellite measurements. Large datasets were used for network training to make the retrieval robust to random errors in the reference data. For each retrieved profile, an effective quality indicator (QI) was obtained by training against the absolute value of the retrieval error. In addition, a reliable rain cloud flag was generated based on the scattering difference of cloud particles between the 50 and 118 GHz channels. Besides the independent reanalysis of field data, the algorithm’s performance was also evaluated using radiosonde measurements. Preliminary validation shows that the best temperature retrieval occurred in the mid-troposphere (around 1.0 K). Depending on the reference data, errors in the boundary layer typically ranged from 1.8 to 2.5 K. For water vapor, the retrieval error was less than 22% in the low-troposphere and less than 35% in the mid- and upper-troposphere when validated against the reanalysis field. The humidity error was approximately 10% lower when compared to radiosondes. The PDNN is used operationally to produce atmospheric temperature and moisture soundings from the Vertical Atmospheric Sounding System (VASS) of the FengYun-3E (FY-3E) satellite, an early-morning-orbit meteorological satellite launched in 2021.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Estimating Near-Surface Concentrations of Major Air Pollutants From Space:
           A Universal Estimation Framework LAPSO

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      Authors: Songyan Zhu;Jian Xu;Meng Fan;Chao Yu;Husi Letu;Qiaolin Zeng;Hao Zhu;Hongmei Wang;Yapeng Wang;Jiancheng Shi;
      Pages: 1 - 11
      Abstract: Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( $text{R}^{2}>$ 0.8 in polluted areas and uncertainty $ll 5~mu text{g}/text{m}^{3}$ for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolutio- is expected to serve the continuous and dynamical monitoring of regional and global air pollution.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Improving the Accuracy of MODIS Near-Infrared Water Vapor Product Under
           all Weather Conditions Based on Machine Learning Considering Multiple
           Dependence Parameters

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      Authors: Jiafei Xu;Zhizhao Liu;
      Pages: 1 - 15
      Abstract: We developed six machine-learning-based calibration models to improve the all-weather accuracy of precipitable water vapor (PWV) product from near-infrared (NIR) observations of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, i.e., MOD05 PWV. The six machine learning approaches are: back propagation neural network (BPNN), gradient boosting decision tree (GBDT), generalized regression neural network (GRNN), K-nearest neighbor (KNN), multilayer perceptron neural network (MLPNN), and extreme gradient boosting (XGBoost). The input of the models included MOD05 PWV, latitude, longitude, elevation, cloud, season, and solar zenith angle, in association with the quality of the MOD05 PWV product. PWV data measured from in situ 453 Global Positioning System (GPS) stations in Australia in 2017 were utilized as the target water vapor data for model training. The validation results in Australia in 2018–2019 indicate that the models can significantly improve the all-weather quality [increased $R^{2}$ , reduced root-mean-square error (RMSE), and reduced mean bias (MB)] of the MOD05 PWV product, exhibiting a reduction in RMSE of 53.33% for BPNN, 55.25% for GBDT, 53.24% for GRNN, 37.81% for KNN, 54.98% for MLPNN, and 55.16% for XGBoost. The $R^{2}$ was 0.58–0.79 and the MB was 0.12–0.72 mm, much better than the MOD05 all-weather PWV product ( $R^{2} = 0.26$ and MB = −1.75 mm). Different from previous studies that focused on clear-sky conditions only, this work is the first one to enhance the quality of official MODIS NIR PWV products under all-weather conditions, reducing the impact of clouds on PWV products.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Cloud and Haze Mask Algorithm From Radiative Transfer Simulations
           Coupled With Machine Learning

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      Authors: Yingzi Jiao;Ming Zhang;Lunche Wang;Wenmin Qin;
      Pages: 1 - 16
      Abstract: Mainstream satellite cloud masking algorithms are prone to mis-masking in haze-polluted areas, which may cause errors in aerosol radiative effect calculations and attribution of surface solar radiance changes; thereby, distinguishing between clouds and haze is critical to obtaining accurate land and atmospheric data products. Existing cloud and haze mask algorithms based on the threshold method may require us to spend a lot of manpower to perform multiple threshold tests; in addition, the obtained thresholds are only applicable to particular sensors, which limits the generality of the threshold-based cloud and haze mask algorithms. In this study, a new cloud and haze mask algorithm based on a combination of radiative transfer simulations and machine learning text simulation-based cloud and haze masking (SCHM) is proposed and applied to MODIS images. When we simulated the apparent reflectance of the first seven visible and text near-infrared channels of MODIS, the CALIOP and AERONET data verification results showed that the SCHM algorithm achieved 85.16% and 90.08% hit rates for cloud and haze recognition, respectively. When we added three thermal infrared channels (20, 31, and 35 bands) for simulation, the cloud and haze hit rates were improved to approximately 85.72% and 90.62%, respectively. This indicates that the SCHM algorithm can improve the accuracy of detection results by improving the radiative transfer simulation parameters. Compared with existing threshold-based methods, the SCHM algorithm has the advantages of simple logic, convenient modification, and flexible configuration.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Modeling of Rain Drop Size Distribution in Association With Convective and
           Cloud Parameter Over a Tropical Location

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      Authors: Arijit De;Animesh Maitra;
      Pages: 1 - 9
      Abstract: The difference in rain drop size distribution (DSD) characteristics between convective and stratiform rain and in monsoon and premonsoon season have been investigated over a tropical location, Kolkata, using seven years of data collected from a Joss–Waldvogel disdrometer (JWD). During the Indian summer monsoon (ISM) and the premonsoon season, Kolkata gets substantial rain. Interannual variability of DSD has been evident. The normalized drop size distributions of DSD show a double peak at 20–50 mm/h rain rate due to drop break up and coalescence process. For convective rain, the concentration of higher drops ( $>$ 3 mm) is more significant compared to stratiform rain. In the premonsoon period, a significant contribution of normalized DSD in the afternoon to late evening has been evident due to nor’westers. The contribution of normalized DSD of higher drops ( $>$ 3 mm) is more in the premonsoon convective rain compared to monsoon convective rain. The larger slope of $mu $ – $Lambda $ relationship has been observed in the premonsoon convective period which corresponds to higher $D_{m}$ values. The relationship between radar reflectivity ( $Z$ ) and rain rate ( $R$ ) distinguishes the monsoon and premonsoon seasons. Radiometric measurements have been used to examine the effects of the atmospheric instability parameter convective available potential energy (CAPE) and cloud-base height (CBH) on the formation of larger drop sizes. This study will be useful for understanding precipitation microphysics and improving the quantitative precipitation estimation (QPE) algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Responses of GNSS ZTD Variations to ENSO Events and Prediction Model Based
           on FFT-LSTME

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      Authors: Tengli Yu;Ershen Wang;Shuanggen Jin;Yong Wang;Jing Huang;Xiao Liu;Wei Zhan;
      Pages: 1 - 17
      Abstract: The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO event’s effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time–frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (−1.12, 1.92) for the tropical monsoon zone (TPMZ), (−1.12, 1.61) for the subtropical monsoon zone (SMZ), (−1.19, 1.62) for the temperate monsoon zone (TMZ), (−1.26, 1.64) for the temperate continental zone (TCZ), and (−1.22, 1.72) for the mountain plateau zone (MPZ). The ENSO event causes the amplitude of the nine-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed by integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Assessment on the Diurnal Cycle of Cloud Covers of Fengyun-4A
           Geostationary Satellite Based on the Manual Observation Data in China

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      Authors: Yongen Liang;Min Min;Yu Yu;Wang Xi;Pan Xia;
      Pages: 1 - 18
      Abstract: Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors in the cloud mask (CLM) algorithm of the Geostationary (GEO) meteorological satellite imaging system, which are very difficult to be assessed by using analogous products of fixed-passing polar-orbiting satellites. In this investigation, the diurnal cycle of the performance of the CLM algorithm of the Advanced Geosynchronous Radiation Imager onboard the China Fengyun-4A satellite (FY-4A/AGRI) is validated by using manually observed cloud covers (CC) at 25 ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI CLM algorithm are overestimated at 11:00 BJT (Beijing Time) and 14:00 BJT (around noon) and underestimated at 08:00 BJT and 20:00 BJT (in the morning and evening) at most stations. In summer, compared with other seasons, the CCs obtained from the FY-4A/AGRI over northern China and the Tibetan Plateau are much better, consistent with the manual observations, but the situation is the opposite in southern China. The CC results retrieved at the vegetation surface by FY-4A/AGRI, however, show the best and stable performance. Because of that, the two independent cloud tests induce most of the overestimations, and some sensitivity experiments for the CLM algorithm are conducted. The results show that the best improvement effect is achieved after only closing one cloud test using the $3.8mu text{m}$ band. Many extremely overestimated CC samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI CLM product is more reasonable compared with the corresponding infrared and visible imageries.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian
           Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

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      Authors: Tianhao Zhang;Huanfeng Shen;Xinghui Xia;Lunche Wang;Feiyue Mao;Qiangqiang Yuan;Yu Gu;Bin Zhao;Zhongmin Zhu;Yun Lin;Yanchen Bo;Wei Gong;
      Pages: 1 - 15
      Abstract: High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellites possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments of their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by a relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, most of them aim at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with the sun-synchronous orbit (SSO) satellite observations based on the Bayesian maximum entropy (BME) theorem is developed for GEO Himawari-8 Advanced Himawari Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other GEO satellit-s over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Dehazing Method for Remote Sensing Image Under Nonuniform Hazy Weather
           Based on Deep Learning Network

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      Authors: Bo Jiang;Jinshuai Wang;Yuwei Wu;Shuaibo Wang;Jinyue Zhang;Xiaoxuan Chen;Yaowei Li;Xiaoyang Li;Lin Wang;
      Pages: 1 - 17
      Abstract: Different from the ground image with uniform haze, the haze in remote sensing (RS) image has the characteristics of irregular shape and uneven concentration in hazy weather. It brings a great challenge to the application of RS image data in advanced image processing tasks. A novel dehazing network for nonuniform hazy RS images, named KFA-Net, is proposed to solve the aforementioned issues. The designed asymmetric size feature cascade (ASFC), k-means pixel attention (KPA), and FFT channel attention (FCA) in KFA-Net all show excellent effects. Compared with symmetrically linked typical Unet, ASFC can more easily extract shallow features for feature reconstruction. Furthermore, different from the commonly used pixel attention that compresses feature maps directly, KPA introduces the k-means clustering algorithm in machine learning into the attention mechanism, which facilitates network training to focus on the thick hazy region. Compared to a typical squeeze-and-excitation block, FCA uses the low-frequency region feature of the spectrogram to obtain the attention weight coefficient in the frequency domain, making network training pay more attention to the feature of the image low-frequency region. Extensive comparison experiments verify that the proposed KFA-Net has great superiority. Peak signal to noise ratio (PSNR) and structural similarity (SSIM) of KFA-Net are 31.0952% and 6.6401% higher than DCP with the highest citation in traditional dehazing methods, respectively. PSNR and SSIM of KFA-Net are 2.2049% and 0.4966% higher than the recently proposed 4KDehazing with the best performance among all comparison dehazing methods, respectively. The KFA-Net proposed in this research can greatly enhance the temporal and spatial scopes of RS image application in hazy weather conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Satellite Aerosol Retrieval From Multiangle Polarimetric Measurements:
           Information Content and Uncertainty Analysis

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      Authors: Wenhui Dong;Minghui Tao;Xiaoguang Xu;Jun Wang;Yi Wang;Lunche Wang;Yinyu Song;Meng Fan;Liangfu Chen;
      Pages: 1 - 13
      Abstract: The multiangle polarimetric (MAP) instruments have been a focus of recent satellite missions dedicated to enhanced detection of global aerosol microphysical properties. Considering that satellite observations can hardly infer all the unknowns of atmosphere and surface, it is crucial to know how many and which aerosol parameters can be accurately retrieved from these different MAP measurements as well as their uncertainties. In this study, we present a comprehensive insight into the information content of POLarization and Directionality of Earth Reflectance-3 (POLDER-3) and multiviewing, multichannel, multipolarization imager (3MI) observations for aerosol retrievals and estimate posterior errors of corresponding parameters based on the Bayesian theory. The total degree of freedom for signal (DFS) of aerosol retrievals is around 6–8 from POLDER-3 and is raised by $sim $ 1.8–3.5 with 3MI. The retrieval accuracy of volume concentration and effective radius is high (< 4%) in the fine-dominant case for both POLDER-3 and 3MI but gets much lower ( $sim $ 8% and $sim $ 15%) in coarse-dominant conditions. Furthermore, the advanced 3MI measurements can upgrade the retrieval uncertainties of POLDER-3 by $sim $ 50%. Though additional shortwave infrared bands of 3MI provide more information regarding coarse particles, the influence of aerosols on surface bidirectional reflectance distribution function (BRDF) leads to a decrease in the total DFS. With a prior assumption that variations of refractive index depend on wavelength, satellite retrieval accuracy of the -eal part (MR) (< 0.03) and imaginary part (MI) (< 0.003) reaches close levels with that of ground-based Sun photometers. Our results can provide a fundamental reference for MAP satellite retrieval of aerosol microphysical properties.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • MM-RNN: A Multimodal RNN for Precipitation Nowcasting

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      Authors: Zhifeng Ma;Hao Zhang;Jie Liu;
      Pages: 1 - 14
      Abstract: Precipitation nowcasting, the high-resolution forecasting of precipitation in a short term, is essential in various applications in the real world. Previous deep learning methods use huge samples to learn potential laws, and the learning process lacks regularity, making it difficult to model the complex nonlinear precipitation phenomenon. Inspired by traditional numerical weather prediction models, we propose the multimodal recurrent neural network (MM-RNN), which introduces knowledge of elements to guide precipitation prediction. This constraint forces the movement of precipitation to follow the underlying atmospheric motion laws. MM-RNN not only can provide accurate precipitation nowcasting but other meteorological elements’ predictions. Besides, it has high flexibility and is compatible with multiple RNN models, such as ConvLSTM, PredRNN, MIM, and MotionRNN. We conduct experiments on two multimodal datasets (MeteoNet and RAIN-F), and the results indicate that MM-RNN is superior to common RNN [multiscale RNN (MS-RNN)] using a single radar modality. For the MeteoNet, compared to MS-MotionRNN, the critical success index (CSI) ( $Rgeqslant 10$ ) of MM-MotionRNN increases by 23.4%, and the mean square error (mse) of MM-MotionRNN decreases by 6.7%. For the RAIN-F, compared to MS-MIM, the HSS ( $Rgeqslant 5$ ) of MM-MIM increases by 209.4%, and the balanced mse (B-MSE) of MM-MIM decreases by 4.6%.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Error Analysis of Rainfall Inversion Based on Commercial Microwave Links
           With A–R Relationship Considering the Rainfall Features

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      Authors: Kang Pu;Xichuan Liu;Xuejin Sun;Shulei Li;
      Pages: 1 - 12
      Abstract: Rainfall inversion based on commercial microwave links (CMLs) has been extensively studied and experimented. However, as necessary part of inversion, the error caused by the attenuation (dB/km)–rainrate (mm/h) ( $A$ – $R$ ) relationship is lack of systematic evaluation. Based on the measured raindrop size distribution (RSD) data recorded by a disdrometer, the theoretical rain-induced attenuation and rainrate are calculated, and on this basis, the error between the inversed rainrate based on $A$ – $R$ relationship from International Telecommunication Union-Recommendation (ITU-R) recommendation and the real rainrate is compared under different rainfall types, rainfall classes, and spatially heterogeneous rain cell. First, the errors of $A$ – $R$ relationship under different rainfall classes show that $A$ – $R$ relationship can effectively invert rainrate between 18 and 26 GHz for all classes, while the error increases significantly outside this frequency range, especially for heavy rainfall. In addition, the $A$ – $R$ relationship performance of different rainfall types is evaluated. There is a significant difference in the unbiasedness of convective -ainfall and stratiform rainfall, which are positive and negative bias at high frequency (greater than 50 GHz), respectively. Furthermore, aiming at the inhomogeneity of rainfall spatial distribution, the rainrate distribution and the corresponding path rain-induced attenuation are simulated based on an exponential model, and the influence of spatial location difference on the accuracy of $A$ – $R$ relationship is deeply analyzed. Finally, the error source and influence of localized $A$ – $R$ relationship, integration time, and link length are discussed.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Automatic SAR Ship Detection Based on Multifeature Fusion Network in
           Spatial and Frequency Domains

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      Authors: Shiyu Wang;Zhanchuan Cai;Jieyu Yuan;
      Pages: 1 - 11
      Abstract: Synthetic aperture radar (SAR) ship detection is sensitive to the interference of inshore background, disturbance of strong wind and waves. The similar textures of the neighbor objects in SAR images affect the detection performance. As a remarkable indicator, textural information in the frequency domain characterizes the subtle textural differences between an object and its surroundings. Inspired by this, a multifeature fusion network (MFFN) for SAR ship detection is constructed in this article, which can obtain contour and detail information of an SAR image for detecting ships from their background. First, spatial and frequency information of ship targets, which characterizes the whole and subtle textural information of ship targets, are extracted by a double-backbone network with Haar wavelet transform. Afterward, a binary domain feature pyramid network (BDFPN) with feature fusion block (FFB) is applied to fuse the spatial, frequency textural information of ship targets to obtain the fused feature maps with a top-down structure. Finally, those feature maps are adopted through the region proposal network (RPN) for detecting ship targets from original images. The experimental results show that the proposed method achieves greater performance and more accurate detection results in unique situations in the state-of-the-art SAR ship detection dataset (SSDD).
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Pixel-CRN: A New Machine Learning Approach for Convective Storm Nowcasting

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      Authors: Wei Zhang;Haonan Chen;Lei Han;Rui Zhang;Yurong Ge;
      Pages: 1 - 12
      Abstract: The short-term convective storm forecasting (i.e., nowcasting) mainly relies on weather radar, which can resolve the 3-D structure of convective storms. With the rapid development of numerical models, modern models can produce 3-D reanalysis data, which gives atmospheric background information of convective storms. Current deep learning nowcasting models use only 2-D radar images for nowcasting and often require massive historical data for training. But, it may not be operationally feasible to collect long-term radar data to train a new model. Hence, how to establish a nowcasting model using only a small dataset has become an important issue. In addition, the existing models do not effectively use the state-of-the-art model reanalysis data, which is a shortcoming of these models. To tackle these problems, this article develops a pixelwise convolutional-recurrent neural network (Pixel-CRN) for precipitation nowcasting. It has three key designs: 1) through a concise pixelwise sampling and oversampling technique, Pixel-CRN can be trained using only a small dataset; 2) in spatial learning, Pixel-CRN embeds a spatial convolution subnet into the recurrent unit, which can input raw 3-D radar and model reanalysis data; thus, valuable atmospheric background information can be learned to assist nowcasting; and 3) in spatiotemporal learning, according to the information bottleneck principle, Pixel-CRN builds a heterogeneous encoder–decoder structure to squeeze multichannel 3-D input data into latent space and recurrently generates 30- and 60-min nowcasts. Compared with the existing deep learning nowcasting methods, the experimental results show that the Pixel-CRN can provide skillful results with a rather small training dataset.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Cloud Identification and Properties Retrieval of the Fengyun-4A Satellite
           Using a ResUnet Model

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      Authors: Zhijun Zhao;Feng Zhang;Qiong Wu;Zhengqiang Li;Xuan Tong;Jingwei Li;Wei Han;
      Pages: 1 - 18
      Abstract: The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun-4A (FY4A) satellite has good cloud observation ability, but it still absents all-weather and high-precision official cloud products. This study develops a deep-learning ResUnet model for all-weather retrieval of cloud phase (CLP) and cloud properties using the brightness temperature from water vapor and longwave infrared channels of AGRI. The ResUnet model is trained with the Himawari-8 satellite Level-2 (H8-L2) cloud products as true targets and adopts image-by-image way to learn the spatial structure information of clouds, which compensates for the difficulty of retrieving thick clouds by thermal infrared radiation at night to some extent. On an independent testing dataset, the model has an overall accuracy of 90.64% for CLP identification and performs well at retrieving cloud top height (CTH). Even without using visible and near-infrared radiation, the root-mean-square error of cloud effective radius (CER) and cloud optical thickness (COT) estimations still reaches $7.14 mu text{m}$ and 9.01 in the range of 0–60. To further illustrate the reliability and applicability, CLP and cloud properties provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS) are used as benchmarks to assess the quality of cloud products from FY4A satellite Level-2 (FY4A-L2), H8-L2, and ResUnet model retrieval. The ResUnet model provides a significant improvement over FY4A-L2 for the accuracy of cloud identification and in the quality of CTH products. In the range of 0– $40 mu text{m}$ (0–60), the CER (COT) product of ResUnet model retrieval has a reliable and higher precision that is comparable with H8-L2.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Mine Diversified Contents of Multispectral Cloud Images Along With
           Geographical Information for Multilabel Classification

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      Authors: Dongxiaoyuan Zhao;Qiong Wang;Jinglin Zhang;Cong Bai;
      Pages: 1 - 15
      Abstract: Multispectral multilabel cloud image classification (MSMLCIC) aims to predict a set of labels presented in a multispectral (MS) cloud image, which usually contains more than one cloud type or weather system. However, the exploration of diversified contents reflected by multiple bands of MS image is limited and the consideration of geographical information (time and location information) is insufficient. To cope with the abovementioned problems, this work proposes the multispectral cloud image multilabel classifier with group feature extractor and geo-queries (MS-GoGo). With a group feature extractor, different bands of MS images are processed separately according to the content they reflected, and a group of distinctive yet complementary image features are generated. Geo-queries are responsible for implicitly embedding different labels with time and location information to probe the corresponding similar semantic ingredients. Due to the coarse classification of the existing dataset, a new dataset named LSCIDMR-V2 is generated with fine-grained cloud-type annotation and multichannel data. The experiment shows that, using the group feature extractor and geo-queries, the popular used metric subset accuracy is improved from 40.06 to 42.87 and 44.35, respectively. The proposed method achieves the mean average precision of 82.40, outperforming state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using
           CAPE and GNSS-Derived ZTD Across China

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      Authors: Yang Liu;Yibin Yao;Qingzhi Zhao;Bao Zhang;Sanda Yu;Aiming Yang;
      Pages: 1 - 13
      Abstract: The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE) and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZTD, CAPE, and ER (ZCER) model, a comprehensive analysis model that integrates ZCER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (ten years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/loge(CAPE) and loge(ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD complemented each other not only on the geographical scales to ER in China, but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZCER frequency were elucidated. Our findings validate the strong links between high ZCER intensity and frequency in China on geographical and temporal scales.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Spatial Downscaling Method for Deriving High-Resolution Downward
           Shortwave Radiation Data Under All-Sky Conditions

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      Authors: Wei Zhao;Wei Wang;Ji Zhou;Lirong Ding;Daijun Yu;
      Pages: 1 - 11
      Abstract: Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3-km MSG DSR data were spatially downscaled to 30-m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3-km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions, and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation with in situ measurements also revealed the significant improvement in terms of the coefficient of determination ( $R^{2}$ ) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/ $text{m}^{2}$ ). In general, the proposed downscaling method in this study shows good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Studies Regarding the Ensquared Energy of a Geostationary Hyperspectral
           Infrared Sounder

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      Authors: Zhenglong Li;Timothy J. Schmit;Jinlong Li;Jun Li;Andrew K. Heidinger;
      Pages: 1 - 9
      Abstract: Compared with low Earth orbit (LEO) satellites, the high altitude from geostationary Earth orbit (GEO) satellites leads to increased diffraction effects on hyperspectral infrared (HIR) sounders, which reduces the ensquared energy (EE) within the satellites’ field of view (FOV) and increases the pseudo noise of the measurements. To help understand how the instrument performance is affected by EE for the Geostationary Extended Observations Sounder (GXS), a point spread function (PSF) is used to simulate the contribution of each location within and outside of an FOV ( $4times $ 4 km at nadir). The PSF is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS) data with a spatial resolution of 50 m, to determine an appropriate EE for GXS. Although wavenumber dependent, an EE of 70% is recommended, which ensures that all GXS channels have pseudo noise less than the instrument specifications. Regardless of the EE value, the pseudo noise reduces the precision of the temperature and moisture sounding retrievals in the troposphere. Even with an EE of 70%, the pseudo noise slightly increases the root-mean-square error (RMSE) by 3%–4% for temperature and by 1%–4% for relative humidity. If an EE of 70% is difficult to meet, due to cost for example, a lower EE can be a good tradeoff with only a slight degradation in the sounding retrieval quality, which may be overcome with spatial averaging using the inverted cone method. An EE of 50% would lead to an RMSE increase of about 6% for temperature and 3%–6% for relative humidity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • The Status and Influencing Factors of Surface Water Dynamics on the
           Qinghai-Tibet Plateau During 2000–2020

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      Authors: Qinwei Ran;Filipe Aires;Philippe Ciais;Chunjing Qiu;Ronghai Hu;Zheng Fu;Kai Xue;Yanfen Wang;
      Pages: 1 - 14
      Abstract: The Qinghai–Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai–Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai–Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km2, and there was a significant increasing trend (=322 km2/year, $p < 0.01$ ) during 2000–2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and-protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • High Latitude Sea Surface Skin Temperatures Derived From Saildrone
           Infrared Measurements

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      Authors: Chong Jia;Peter J. Minnett;Malgorzata Szczodrak;Miguel Izaguirre;
      Pages: 1 - 14
      Abstract: From May 15 to October 11, 2019, six Saildrone uncrewed surface vehicles (USVs) were deployed for 150-day cruises collecting a suite of atmospheric and oceanographic measurements from Dutch Harbor, Alaska, transiting the Bering Strait into the Chukchi Sea and the Arctic Ocean. Two Saildrones funded by the National Aeronautics and Space Administration (NASA), SD-1036 and SD-1037, were equipped with infrared (IR) radiation pyrometers in a “unicorn” structure on the deck for the determination of the ocean sea surface skin temperature (SST $_{mathrm {skin}}$ ). We present an algorithm to derive SSTskin from the downward- and upward-looking radiometers and estimate the main contributions to the inaccuracy of SSTskin. After stringent quality control of data and eliminating measurements influenced by sea ice and precipitation, and restricting the acceptable tilt angles of the USV based on radiative transfer simulations, SSTskin can be derived to an accuracy of approximately 0.12 K. The error budget of the derived SSTskin is developed, and the largest component comes from the instrumental uncertainties, assuming that the viewing geometry is adequately determined. Thus, Saildrones equipped with these sensors could provide sufficiently accurate SSTskin retrievals for studying the physics of the thermal skin effect, in conjunction with accurate subsurface thermometer measurements, and for validating satellite-derived SSTskin fields at high latitudes.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Impact of Rain Effects on L-Band Passive Microwave Satellite Observations
           Over the Ocean

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      Authors: Xuchen Jin;Xianqiang He;Difeng Wang;Jianyun Ying;Fang Gong;Qiankun Zhu;Chenghu Zhou;Delu Pan;
      Pages: 1 - 16
      Abstract: L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good ac-uracy for rain effect correction.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Significant Wave Height Retrieval Based on Multivariable Regression Models
           Developed With CYGNSS Data

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      Authors: Changyang Wang;Kegen Yu;Kefei Zhang;Jinwei Bu;Fangyu Qu;
      Pages: 1 - 15
      Abstract: This study utilizes L1B level data from reflected global navigation satellite system (GNSS) signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave height (SWH). The normalized bistatic radar cross Section (NBRCS), the leading edge slope (LES), the signal-to-noise ratio (SNR), and the delay-Doppler map average (DDMA) are used as the key variables for the SWH retrieval. Eight other parameters, including instrument gain and scatter area, are also utilized as auxiliary variables to enhance the SWH retrieval performance. A variety of multivariable regression models are investigated to clarify the relationship between the SWH and the variables by using the following five methods: stepwise linear regression, Gaussian support vector machine, artificial neural network, sparrow search algorithm–extreme learning machine, and bagging tree (BT). Results show that, among the five regression models developed, the BT model performs the best with the root mean square error (RMSE) of 0.48 m and the correlation coefficient (CC) of 0.82 when testing one million sets of data randomly selected, while the RMSE and CC of BT model are 0.44 m and 0.73 in the 4500 National Data Buoy Center (NDBC) buoy testing dataset. Meanwhile, the BT model also has the best generalization ability, which means that it performs well in practical applications. In addition, the impacts of different input variables, the size of the training dataset, and the sea surface wind speed are also investigated. These findings are anticipated to serve as helpful guides for creating future SWH retrieval algorithms that are more advanced.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Reconstruction of 3-D Ocean Chlorophyll a Structure in the Northern Indian
           Ocean Using Satellite and BGC-Argo Data

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      Authors: Qiwei Hu;Xiaoyan Chen;Yan Bai;Xianqiang He;Teng Li;Delu Pan;
      Pages: 1 - 13
      Abstract: We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll $a$ (Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with $R^{2} = 0.962$ , root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • 2-D Magnetic Resonance Tomography With an Inaccurately Known Larmor
           Frequency Based on Frequency Cycling

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      Authors: Jiannan Liu;Baofeng Tian;Chuandong Jiang;Ruixin Miao;Yanju Ji;
      Pages: 1 - 13
      Abstract: When using magnetic resonance tomography (MRT) for imaging 2-D or 3-D water-bearing structures in a subsurface, the transmitting frequency must be the same as the Larmor frequency. Due to the inhomogeneity and noise interference in a geomagnetic field, it is difficult to determine the precise Larmor frequency using a magnetometer, resulting in unknown frequency offsets and inaccurate estimations of water content and relaxation time ( $T_{2}^{*}$ ). To solve the 2-D MRT imaging problem in the case of an unknown frequency offset, a frequency cycling method is proposed in this article. This method takes the estimated Larmor frequency as the center, uses two frequencies with the same offset for transmitting, then combines the acquired MRT signals to obtain frequency-cycled data, and finally uses the off-resonance kernel function for inversion. Based on MRT forward modeling and QT inversion, we conduct synthetic data experiments on a complex model with three water-bearing structures and test the 2-D imaging results of the frequency-cycled data. The results show that the water content and $T_{2}^{*}$ distribution obtained by the inversion of the frequency-cycled data can accurately reflect the water-bearing structure, which is better than the results of the assumed on-resonance case. In addition, the phase correction method presented in this article significantly improves the accuracy of 2-D MRT estimated aquifer properties under low resistivity conditions. Finally, the validity and accuracy of the frequency cycling method are verified by comparing the inversion results of the data with known drilling data collected in field measurements.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Per-Pixel Uncertainty Quantification and Reporting for Satellite-Derived
           Chlorophyll-a Estimates via Mixture Density Networks

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      Authors: Arun M. Saranathan;Brandon Smith;Nima Pahlevan;
      Pages: 1 - 18
      Abstract: Mixture density networks (MDNs) have emerged as a powerful tool for estimating water-quality indicators, such as chlorophyll-a (Chl $a$ ) from multispectral imagery. This study validates the use of an uncertainty metric calculated directly from Chl $a$ estimates of the MDNs. We consider multispectral remote sensing reflectance spectra ( $R_{text {rs}}$ ) for three satellite sensors commonly used in aquatic remote sensing, namely, the ocean and land colour instrument (OLCI), multispectral instrument (MSI), and operational land imager (OLI). First, a study on a labeled database of colocated in situ Chl $a$ and $R_{text {rs}}$ measurements clearly illustrates that the suggested uncertainty metric accurately captures the reduced confidence associated with test data, which is drawn for a different distribution than the training data. This change in distribution maybe due to: 1) random noise; 2) uncertainties in the atmospheric correction; and 3) novel (unseen) data. The experiments on the labeled in situ dataset show that the estimated uncertainty has a correlation with the expected predictive error and can be used as a bound on the predictive error for most samples. To illustrate the ability of the MDNs in generating consistent products from multiple sensors, per-pixel uncertainty maps for three near-coincident images of OLCI, MSI, and OLI are produced. The study also examines temporal trends in OLCI-derived Chl $a$ and the associated uncertainties at selected locations over a calendar year. Future work will include unce-tainty estimation from MDNs with a multiparameter retrieval capability for hyperspectral and multispectral imagery.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Spectral Harmonization Algorithm for Landsat-8 and Sentinel-2 Remote
           Sensing Reflectance Products Using Machine Learning: A Case Study for the
           Barents Sea (European Arctic)

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      Authors: Muhammad Asim;Atsushi Matsuoka;Pål Gunnar Ellingsen;Camilla Brekke;Torbjørn Eltoft;Katalin Blix;
      Pages: 1 - 19
      Abstract: The synergistic use of Landsat-8 operational land imager (OLI) and Sentinel-2 multispectral instrument (MSI) data products provides an excellent opportunity to monitor the dynamics of aquatic ecosystems. However, the merging of data products from multisensors is often adversely affected by the difference in their spectral characteristics. In addition, the errors in the atmospheric correction (AC) methods further increase the inconsistencies in downstream products. This work proposes an improved spectral harmonization method for OLI and MSI-derived remote sensing reflectance ( ${R_{rs}}$ ) products, which significantly reduces uncertainties compared to those in the literature. We compared ${R_{rs}}$ retrieved via state-of-the-art AC processors, i.e., Acolite, C2RCC, and Polymer, against ship-based in situ ${R_{rs}}$ observations obtained from the Barents Sea waters, including a wide range of optical properties. Results suggest that the Acolite-derived ${R_{rs}}$ has a minimum bias for our study area with median absolute percentage difference (MAPD) varying from 9% to 25% in the blue–green bands. To spectrally merge OLI and MSI, we develop and apply a new machine learning-based bandpass adjustment (BA) model to near-simultaneous OLI and MSI images acquired in the years from 2018 to 2020. Compared to a conventional linear adjustment, we demonstrate that the spectral difference is significantly reduced from $sim 6$ % to 12% to $sim 2$ %- to ${< } {10%}$ in the common OLI-MSI bands using the proposed BA model. The findings of this study are useful for the combined use of OLI and MSI ${R_{rs}}$ products for water quality monitoring applications. The proposed method has the potential to be applied to other waters.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep Multi-Look Sequence Processing for Synthetic Aperture Sonar Image
           Segmentation

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      Authors: Isaac D. Gerg;Vishal Monga;
      Pages: 1 - 15
      Abstract: Deep learning has enabled significant improvements in semantic image segmentation, especially in underwater imaging domains such as side scan sonar (SSS). In this work, we apply deep learning to synthetic aperture sonar (SAS) imagery, which has an advantage over traditional SSS in which SAS produces coherent high- and constant-resolution imagery. Despite the successes of deep learning, one drawback is the need for abundant labeled training data to enable success. Such abundant labeled data are not always available as in the case of SAS where collections are expensive and obtaining quality ground-truth labels may require diver intervention. To overcome these challenges, we propose a domain-specific deep learning network architecture utilizing a unique property to complex-valued SAS imagery: the ability to resolve angle-of-arrival (AoA) of acoustic returns through $k$ -space processing. By sweeping through consecutive incrementally advanced AoA bandpass filters (a process sometimes referred to as multi-look processing), this technique generates a sequence of images emphasizing angle-dependent seafloor scattering and motion from biologics along the seafloor or in the water column. Our proposal, which we call multi-look sequence processing network (MLSP-Net), is a domain-enriched deep neural network architecture that models the multi-look image sequence using a recurrent neural network (RNN) to extract robust features suitable for semantic segmentation of the seafloor without the need for abundant training data. Unlike previous segmentation works in SAS, our model ingests a complex-valued SAS image and affords the ability to learn the AoA filters in $k$ -space as part of the training procedure. We show the results on a challenging real-world SAS database, and despite the lack of abundant training data, our proposed method shows-superior results over state-of-the-art techniques.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • On the Use of Dual Co-Polarized Radar Data to Derive a Sea Surface Doppler
           Model—Part 1: Approach

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      Authors: Vladimir Kudryavtsev;Shengren Fan;Biao Zhang;Bertrand Chapron;Johnny A. Johannessen;Artem Moiseev;
      Pages: 1 - 13
      Abstract: This article proposes a Doppler velocity (DV) model based on dual co-polarized (co-pol) decomposition of a normalized radar cross section of an ocean surface on polarized Bragg scattering and nonpolarized (NP) radar returns from breaking wave components. The dual co-pol decomposition provides a quantitative description of resonant and NP scattering, as well as their dependence on the incident angle, azimuth, and wind speed. Subsequently, the contributions of the facet (resonant Bragg waves and breakers) velocities, tilt, and hydrodynamic modulations due to long waves to the resulting DV can be quantified. The tilt modulation contributions to DV are estimated using the measured/empirical tilt modulation transfer function (MTF). The hydrodynamic modulations are mostly dominated by wave breaking and are estimated using a semiempirical model based on in situ measurements. In addition to the VV and HH radar data, which are required for dual co-pol decomposition and tilt MTF estimates, the surface wave spectrum is required in the DV determination for a given radar observation geometry. In this article, qualitative and quantitative consistencies are presented between the model simulations and the empirical CDOP model. In a companion paper, a DV analysis is presented to analyze the Sentinel-1 synthetic aperture radar measurements and collocated in situ measurements of surface wind and wave spectra.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Image-to-Height Domain Translation for Synthetic Aperture Sonar

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      Authors: Dylan Stewart;Austin Kreulach;Shawn F. Johnson;Alina Zare;
      Pages: 1 - 13
      Abstract: Synthetic aperture sonar (SAS) intensity statistics are dependent upon the sensing geometry at the time of capture. Estimating bathymetry from acoustic surveys is challenging. While several methods have been proposed to estimate seabed relief via intensity, we develop the first large-scale study that relies on deep learning models. In this work, we pose bathymetric estimation from SAS surveys as a domain translation problem of translating intensity to height. Since no dataset of coregistered seabed relief maps and sonar imagery previously existed to learn this domain translation, we produce the first large simulated dataset containing coregistered pairs of seabed relief and intensity maps from two unique sonar data simulation techniques. We apply four types of models, with varying complexity, to translate intensity imagery to seabed relief: a shape-from-shading (SFS) approach, a Gaussian Markov random field (GMRF) approach, a conditional Generative Adversarial Network (cGAN), and UNet architectures. Each model is applied to datasets containing sand ripples, rocky, mixed, and flat sea bottoms. Methods are compared in reference to the coregistered simulated datasets using L1 error. Additionally, we provide results on simulated and real SAS imagery. Our results indicate that the proposed UNet architectures outperform an SFS, a GMRF, and a pix2pix cGAN model.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Assessing the Relative Performance of GNSS-R Flood Extent Observations:
           Case Study in South Sudan

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      Authors: Brandi Downs;Albert J. Kettner;Bruce D. Chapman;G. Robert Brakenridge;Andrew J. O’Brien;Cinzia Zuffada;
      Pages: 1 - 13
      Abstract: Flooding is one of the deadliest and costliest natural disasters. Climate change-induced flooding events are increasing worldwide, disproportionately impacting low-income and developing communities. While early warning systems save lives, satellite-based observation systems are vital for the disaster relief and recovery phases. Current satellite-based operational flood products are largely based on either optical remote-sensing methods, which exhibit a limited ability to detect water through clouds and vegetation, or microwave remote sensing, which provides relatively low spatial and temporal resolution. New small satellite constellations using radar or GNSS reflectometry (GNSS-R) have been shown to enhance our ability to overcome these deficiencies. In this work, we quantify the performance of using GNSS-R measurements from the NASA Cyclone Global Navigation Satellite Systems (CYGNSS) satellite constellation to map surface water in South Sudan and the Sudd wetland in comparison with a set of representative operational products. We make quantitative comparisons of our results with operational flood products based on Visible Infrared Imaging Radiometer Suite (VIIRS) and MODIS and with C-band Sentinel-1 synthetic aperture radar. We find that our method detects 35.4% more surface water than Sentinel-1, while the VIIRS- and MODIS-based products underestimate by 4.8% and 83.7%, respectively. We use several metrics commonly used to evaluate classification performance: precision, true positive rate (TPR), true negative rate (TNR), F2-score, and the Matthews correlation coefficient (MCC) and assess the comparisons in this statistical framework. We discuss the consequences of our findings, including ways CYGNSS data may enhance current flood products and assist decision-makers and emergency managers.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • DGNet: Distribution Guided Efficient Learning for Oil Spill Image
           Segmentation

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      Authors: Fang Chen;Heiko Balzter;Feixiang Zhou;Peng Ren;Huiyu Zhou;
      Pages: 1 - 17
      Abstract: Successful implementation of oil spill segmentation in synthetic aperture radar (SAR) images is vital for marine environmental protection. In this article, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth
           Engine

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      Authors: Ebrahim Hamidi;Brad G. Peter;David F. Muñoz;Hamed Moftakhari;Hamid Moradkhani;
      Pages: 1 - 19
      Abstract: Flooding is one of the most frequent and disastrous natural hazards triggered by extreme precipitation, high river runoff, hurricane storm surges, and the compounding effects of various flood drivers. This study introduces a new multisource remote sensing approach that leverages both multispectral optical imagery and the weather- and illumination-independent characteristics of synthetic aperture radar (SAR) data to streamline, automate, and map geographically reliable flood inundation extents. Utilizing the near real-time and cloud computing capabilities of Google Earth Engine (GEE), this process facilitates data acquisition and enables large-scale flood monitoring in an expeditious manner. Two major hurricanes along the U.S. Gulf Coast were evaluated: 1) the 2021 Hurricane Ida to the south of New Orleans, LA, USA, and 2) the 2017 Hurricane Harvey to the east of Houston, TX, USA. We devised a change detection and thresholding framework using multitemporal SAR imagery and validated the results with flood extent maps derived from Landsat 8 and Sentinel-2 optical imagery. We demonstrate that constant threshold values for flood extraction from SAR change detection indices are not ubiquitously suitable for all geographies; thus, we outline a heuristic that can be used to select thresholds suitable for specific sites through a fully automated sensitivity analysis. The results indicated high agreement between the SAR and optical imagery (77%–80%), with SAR providing the benefit of under-cloud detection. Furthermore, our results contribute to scaling the SAR approach to produce rapid and accurate information for decision-makers and emergency responders during time-sensitive flood events.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • On the Spatial and Temporal Variations of Primary Production in the South
           China Sea

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      Authors: Luping Song;Zhongping Lee;Shaoling Shang;Bangqin Huang;Jinghui Wu;Zelun Wu;Wenfang Lu;Xin Liu;
      Pages: 1 - 14
      Abstract: Primary production (PP) of the South China Sea (SCS) basin area (waters depth deeper than 200 m) is estimated using satellite products, with an overarching goal to reliably characterize the spatial distribution and temporal variation of PP of this important marginal sea. Among the PP models used, the absorption-based model (AbPM) showed better performance ( $R^{2}=0.47$ and $N =39$ ). In comparison, the $R^{2}$ value is 0.26 for a chlorophyll-based model [vertically generalized production model (VGPM)] and 0.15 for the carbon-based model (CbPM). Furthermore, we observed that the PP spatial patterns obtained from these models were similar but disagree on the annual PP magnitude, where VGPM and CbPM, respectively, obtained $sim $ 50% lower and $sim $ 40% higher annual PP compared to that obtained by AbPM. In particular, after analysis using empirical orthogonal functions (EOFs), the upwelling-induced high PP off Luzon (winter) and Vietnam coast (summer) was clearly reflected in the first EOF mode of the AbPM results, and its principal component 1 has shown a decreasing trend for the period of 2003–2019 (−15.0% yr $^{-1}$ for winter, $p < 0.05$ ; −14.7% yr $^{-1}$ for summer, $p < 0.05- ), which reflects the impact of weakening wind and higher sea surface temperature in the SCS. For the results of VGPM and CbPM, however, no strong relationships were found with the main regional oceanographic features. These results suggested that the spatiotemporal variations of SCS PP obtained from AbPM are more reasonable and further highlight the importance of a robust model in reliably capturing large-scale spatiotemporal dynamics of PP in marine environments.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Comprehensive Deep Learning-Based Outlier Removal Method for Multibeam
           Bathymetric Point Cloud

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      Authors: Jiawei Long;Hongmei Zhang;Jianhu Zhao;
      Pages: 1 - 22
      Abstract: To address the drawbacks that current multibeam bathymetric outlier removal methods lack repeatability, often require parameter adjustment for different regions, still require a lot of manual labor, and offer limited scalability, a deep learning-based outlier removal method for multibeam bathymetric data is proposed. The method fully considers the multibeam data measurement principles and causes of outliers in multibeam bathymetric data and includes a comprehensive sample augmentation method and an outlier removal model based on a modification of a recently proposed PCPNet architecture. In our extensive evaluation, both on synthetic and real data, our method demonstrates robust outlier removal performance in a variety of marine environments without any parameter adjustment.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Region-Growing-Based Automatic Localized Adaptive Thresholding Algorithm
           for Water Extraction Using Sentinel-2 MSI Imagery

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      Authors: Bharath Kumar Reddy Kadapala;Abdul Hakeem K;
      Pages: 1 - 8
      Abstract: Water is a distinct land cover feature on the Earth. Water can easily be extracted from satellite data under known/controlled conditions using simple thresholding techniques. However, these pixel thresholding techniques fail when applied over large regions because they cannot adapt the thresholds based on local variability. Although region-growing methods are available to overcome this issue, there is a limitation in using a global threshold value determined for a satellite scene. Such thresholds fail to adapt to the variations within the scene. Hence, this study proposed a new region-growing-based approach for improved classification of water features using Sentinel-2 multispectral instrument (MSI) data. The significant difference between this approach and other region-growing methods is that this technique uses dynamic thresholding for localized adaption. The local image statistics of the normalized difference water index (NDWI) layer are used for adding new pixels to the region, and the threshold is adjusted for every new pixel added. This technique also minimizes manual intervention in water classification, thus enabling total process automation in near real time. The thresholds are dynamically determined not only for each scene but also for each new pixel being classified for the accurate delineation of the water pixels. A rigorous quality evaluation was done on the resultant water layer. This technique achieved an overall accuracy of 84.04% in challenging conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Temporal Fusion Based 1-D Sequence Semantic Segmentation Model for
           Automatic Precision Side Scan Sonar Bottom Tracking

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      Authors: Xiaoming Qin;Ziyin Wu;Xiaowen Luo;Bin Li;Dineng Zhao;Jieqiong Zhou;Mingwei Wang;Hongyang Wan;Xiaolun Chen;
      Pages: 1 - 16
      Abstract: Precision bottom tracking is a core step in the data processing of side scan sonar (SSS), which is of critical value for the quality of the final SSS production. Currently, the automatic precision SSS bottom tracking remains challenging due to the complex noise caused by the measuring environment; especially, the existing methods did not fully exploit the temporal correlations or depended on the hand-crafted setting. Therefore, we proposed a novel temporal fusion-based 1-D sequence semantic segmentation model, TFSSM–1–D, to fuse the temporal correlation features and perform automatic precision SSS bottom tracking. The TFSSM–1–D uses the deep learning (DL) encoder–decoder model for mapping inputs to 1-D semantic label outputs, with the aid of preprocess and temporal fusion modules, to improve the accuracy and robustness. Among them, preprocess module is used to introduce the prior knowledge of bilateral symmetry, which alleviates the defect of long-distance features correlation caused by the inductive bias of convolution neural network (CNN). The temporal fusion consists of the point-wise temporal fusion module (PTFM), and the bi-directional attention propagation module (BAPM) guides the model to explicitly fuse the temporal variation features on different scales. The experimental results demonstrate the effectiveness of TFSSM–1–D, and its mean port offset error (MPOE) reaches 2.7058 on the testing set without downsampling, which is 40% lower than the previous DL based model with single ping input, and other evaluation metrics have also significantly improved. The inference on unseen data shows that TFSSM–1–D can achieve precision bottom tracking with good noise immunity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Bagged-Tree Machine Learning Model for High and Low Wind Speed Ocean
           Wind Retrieval From CYGNSS Measurements

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      Authors: Pin-Hsuan Cheng;Charles Chien-Hung Lin;Y. T. Jade Morton;Shu-Chih Yang;Jann-Yenq Liu;
      Pages: 1 - 10
      Abstract: This article presents two empirical models, the low wind bagged trees (LWBT) and high wind bagged trees (HWBT) ensemble models to estimate ocean surface wind speed using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. The models are empirically trained using NASA’s Cyclone GNSS (CYGNSS) mission level 1 data (version 2.1). The truth label for the LWBT model is the wind speed product derived from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-5 and Global Data Assimilation System (GDAS), while the label for the HWBT model is wind speed measurements from stepped frequency microwave radiometer (SFMR). Testing results show that the LWBT and HWBT models achieved global wind speed retrieval root-mean-square-error (RMSE) of $sim $ 1.5 and $sim $ 1.4 m/s, respectively, corresponding to an improvement of 29% and 65% with respect to the CYGNSS Level 2 standard wind speed product. The maximum bias is reduced by 65% and 60% for LWBT and HWBT over the Level 2 wind speeds, respectively. Two typhoon case studies are presented to corroborate the model performances and their retrieved wind speeds are consistent with reports from World Meteorological Organization (WMO) and with the measurement provided by the Huangmao Zhou (HMZ) weather station.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • On the Use of Dual Co-Polarized Radar Data to Derive a Sea Surface Doppler
           Model—Part 2: Simulation and Validation

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      Authors: Shengren Fan;Biao Zhang;Artem Moiseev;Vladimir Kudryavtsev;Johnny A. Johannessen;Bertrand Chapron;
      Pages: 1 - 9
      Abstract: The Doppler shift obtained from synthetic aperture radar (SAR) measurements comprises the combined contribution to the radial motion of the ocean surface induced by the sea state (wind waves and swell) and underlying surface currents. Hence, to obtain reliable estimates of the ocean surface current (OCS), the sea-state-induced Doppler shifts must be accurately estimated and eliminated. In this study, we use a semiempirical dual co-polarization Doppler velocity (DPDop) model, presented in the companion paper, to calculate sea-state-induced Doppler shifts using buoy-measured wind speed, wind direction, and ocean wave spectra. The DPDop model-simulated Doppler shifts are compared with the collocated Sentinel-1B SAR Wave (WV) mode observations at the 24° and 37° incidence angles, showing a bias of −0.24 Hz and a root-mean-square error (RMSE) of 5.55 Hz. This evaluation is also implemented on a simplified DPDop model at the same incidence angles. The model inputs include wind fields from the European Center for Medium-Range Weather Forecasts (ECMWF) and wave characteristic parameters (e.g., significant wave height (SWH), mean wave direction, and mean wavenumber) from WAVEWATCH III (WW3). The estimated Doppler shifts are validated using the ascending and descending observations of Sentinel-1B WV over the global ocean. Furthermore, the comparisons show that the bias and RMSE are −0.71 and 9.25 Hz, respectively. Based on accurate wave bias correction, we obtain the radial current speeds of the ocean surface from the Doppler shift measurements. The estimated current speeds are compared with the collocated high-frequency (HF) radar measurements, with a bias of −0.04 m/s and an RMSE of 0.15 m/s. These results suggest that the original and simplified DPDop models can be used to estimate sea-state-induced Doppler shifts and, thus, derive accurate surface current retrievals.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Sample Enhancement Method Based on Simple Linear Iterative Clustering
           Superpixel Segmentation Applied to Multibeam Seabed Classification

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      Authors: Haiyang Hu;Chengkai Feng;Xiaodong Cui;Kai Zhang;Xianhai Bu;Fanlin Yang;
      Pages: 1 - 15
      Abstract: Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this article proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a random forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, U.K. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiscale and Multisubgraph-Based Segmentation Method for Ocean Remote
           Sensing Images

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      Authors: Qianna Cui;Haiwei Pan;Kejia Zhang;Xiaokun Li;Haoyu Sun;
      Pages: 1 - 20
      Abstract: Interpreting ocean remote sensing images is still a challenge that is worth studying because they can carry valuable information for various important applications. Due to the absence of labeled datasets, unsupervised object-based image analysis (OBIA) methods provide an effective solution to understand remote sensing images with the advantage of grouping local similar pixels into a homogeneous area. However, ocean remote sensing images usually have the characteristics of large size, large background, and coexisting of large and small objects, which results in previous OBIA methods easily falling into the difficulty of accurately segmenting the large and small objects at the same time and the dilemma of time-consuming computation. To solve this problem, a novel multiscale and multisubgraph (MSMSG)-based image segmentation method is presented in this article. First, a coarse-to-fine superpixel generation method is designed to generate optimal superpixels, which can not only solve the problem of coexisting large objects and small objects but also the problem of manually setting the initial segmentation number. Second, the proposed background removal strategy helps to eliminate the trouble of large background areas in ocean remote sensing images. Third, a multisubgraph is constructed with the help of background removal. Finally, the MSMSG merging strategy is addressed to group all similar superpixels into the same cluster, which not only reduces the useless computation of nonadjacent superpixels but also avoids segmentation errors with the same scale. Experiments conducted on three different datasets show that the proposed segmentation method is high-performance and high-efficiency.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Uncertainty Exploration: Toward Explainable SAR Target Detection

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      Authors: Zhongling Huang;Ying Liu;Xiwen Yao;Jun Ren;Junwei Han;
      Pages: 1 - 14
      Abstract: Deep learning-based synthetic aperture radar (SAR) target detection has been developed for years, with many advanced methods proposed to achieve higher indicators of accuracy and speed. In spite of this, the current deep detectors cannot express the reliability and interpretation in trusting the predictions, which are crucial especially for those ordinary users without much expertise in understanding SAR images. To achieve explainable SAR target detection, it is necessary to answer the following questions: how much should we trust and why cannot we trust the results. With this purpose, we explore the uncertainty for SAR target detection in this article by quantifying the model uncertainty and explaining the ignorance of the detector. First, the Bayesian deep detectors (BDDs) are constructed for uncertainty quantification, answering how much to trust the classification and localization result. Second, an occlusion-based explanation method (U-RISE) for BDD is proposed to account for the SAR scattering features that cause uncertainty or promote trustworthiness. We introduce the probability-based detection quality (PDQ) and multielement decision space for evaluation besides the traditional metrics. The experimental results show that the proposed BDD outperforms the counterpart frequentist object detector, and the output probabilistic results successfully convey the model uncertainty and contribute to more comprehensive decision-making. Furthermore, the proposed U-RISE generates an attribution map with intuitive explanations to reveal the complex scattering phenomena about which BDD is uncertain. We deem our work will facilitate explainable and trustworthy modeling in the field of SAR image understanding and increase user comprehension of model decisions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Reducing Biases in Thermal Infrared Surface Radiance Calculations Over
           Global Oceans

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      Authors: Nicholas R. Nalli;James A. Jung;Robert O. Knuteson;Jonathan Gero;Cheng Dang;Benjamin T. Johnson;Lihang Zhou;
      Pages: 1 - 18
      Abstract: Thermal infrared (IR) environmental satellite data assimilation and remote sensing of the surface and lower troposphere depend on the accurate specification of the spectral surface emissivity within clear-sky forward calculations. Over ocean surfaces, accurate modeling of surface-leaving radiances over the sensor scanning swaths is complicated by a quasi-specular bidirectional reflectance distribution function (BRDF). Recent findings at the Joint Center for Satellite Data Assimilation (JCSDA) have also revealed significant zonally varying systematic biases ( $approx 0.5 $ K) on a global scale over cold ocean waters; these are the results of temperature dependence in the thermal IR optical constants. This article proposes practical solutions to these problems by modeling thermal IR “effective emissivity” in a manner that accounts for both surface emission and quasi-specular reflectance, along with temperature dependence, while meeting the latency and computational constraints of operational global data assimilation and retrieval systems. We overview the theoretical basis of the model and validate it against ship-based Marine Atmospheric Emitted Radiance Interferometer (MAERI) spectra obtained from cold and warm water ocean campaigns.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • High-Precision Underwater Acoustic Localization of the Black Box Utilizing
           an Autonomous Underwater Vehicle Based on the Improved Artificial
           Potential Field

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      Authors: Sibo Sun;Huigong Guo;Guangming Wan;Chao Dong;Ce Zheng;Yong Wang;
      Pages: 1 - 10
      Abstract: Underwater acoustic localization (UWAL) of the black box for a sunken airplane utilizing an autonomous underwater vehicle (AUV) is a useful technique in ensuring traffic safety. Aiming at improving localization precision, this article proposes a new path-planning algorithm based on the improved artificial potential field (APF). Compared with the conventional APF, we modify the conventional gravitation force and introduce a new localization precision force. Therefore, a balance between localization precision and obstacle avoidance is achieved, and the localization precision is significantly improved. The lake trial result validates the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • DensePPMUNet-a: A Robust Deep Learning Network for Segmenting Water Bodies
           From Aerial Images

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      Authors: Deqiang Xiang;Xin Zhang;Wei Wu;Hongbin Liu;
      Pages: 1 - 11
      Abstract: The identification of water bodies from aerial images using semantic segmentation networks can provide accurate information for ecological monitoring, flood prevention, and disaster reduction. Outliers on aerial images might reduce interclass separability and thus cause discontinuous prediction of water bodies. The fusion of global context information is helpful to solve this problem. However, the existing global prior representation does not provide sufficient information for identifying a large number of multiscale objects and outliers. In this study, a dense pyramid pooling module (DensePPM) was introduced to extract global prior knowledge with a dense scale distribution. The ablation experiments showed that the models using the DensePPM had higher values of IoU, $F1$ -score, and recall than that using pyramid pooling module (PPM), showing that the proposed module could capture more global context information of outliers under multiscale scenarios. A robust deep learning network named DensePPMUNet-a based on the DensePPM was then proposed for segmenting water bodies from aerial images. The comparative experiments with different datasets demonstrated that the DensePPMUNet-a outperformed other state-of-the-art deep learning models.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Single-Pass Tropical Cyclone Detector and Scene-Classified Wind Speed
           Retrieval Model for Spaceborne GNSS Reflectometry

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      Authors: Feng Wang;Guodong Zhang;Dongkai Yang;Hui Kuang;
      Pages: 1 - 16
      Abstract: Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been used to retrieve wind speed, but few studies have directly detected tropical cyclones from spaceborne GNSS-R observations. Moreover, it is challenging to solve the multivalued dependence of the spaceborne observable on wind speed to unambiguously and accurately retrieve wind speed in cyclone conditions. This article presents a single-pass cyclone detector and coarse estimation approach of the tropical cyclone position using a spaceborne GNSS-R full delay-Doppler map (FDDM). When the specular point passes through a tropical cyclone, the FDDM asymmetry experiences an abnormal change. From the FDDM asymmetry sequence along the specular point trajectory, two subsequence features, including the slope and extremum difference, are defined, from which the tropical cyclone detector is proposed. The results from the simulation and the Cyclone GNSS (CYGNSS) data both indicate a good detection performance for tropical cyclones. The location corresponding to the peak detector is considered a coarse estimation of the tropical cyclone position. The test result from the CYGNSS data shows that the mean error between the detected cyclone center and the International Best Track Archive for Climate Stewardship (IBTrACS) cyclone center is approximately 124.50 km. From the detector of tropical cyclones, a scene-classified wind speed retrieval model is proposed. The simulated and experimental results show that a better retrieval performance can be obtained at high wind speed ( ${>}30$ m/s) using the scene-classified model. Reductions of 10 and 4 m/s in the root mean square errors (RMSEs) are obtained for the simulation and CYGNSS data, respectively. This work is meaningful for directly detecting tropical cyclones and retrieving high wind speed data using spaceborne GNSS-R in real time.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Noise-Aware Deep Learning Model for Underwater Acoustic Denoising

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      Authors: Aolong Zhou;Wen Zhang;Xiaoyong Li;Guojun Xu;Bingbing Zhang;Yanxin Ma;Junqiang Song;
      Pages: 1 - 13
      Abstract: Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband–subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband–subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight scale-invariant signal-to-noise ratio (SI-SNR) loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Integrating Unordered Time Frames in Neural Networks: Application to the
           Detection of Natural Oil Slicks in Satellite Images

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      Authors: Antoine Scardigli;Laurent Risser;Chihab Haddouche;Romain Jatiault;
      Pages: 1 - 14
      Abstract: In this article, we explore the use of novel neural-network architectures to distinguish natural seepages from artificial slicks in synthetic aperture radar (SAR) images. They exploit a distinctive property of natural seepages, which is their temporal recurrence in the same geographical area. This information can be captured in different SAR images acquired at the same location over time, but not necessarily at a regular time frequency. The proposed neural-network architectures are then built as specific block layers, which efficiently treat the unordered temporal information, followed by more conventional neural-network layers, which are widely used for image classification. Different block layers for unordered temporal information are compared on Sentinel-1 images acquired in the Aegean Sea. Following data augmentation steps, our dataset contains a consistent subset of images gathered among 16000 time-patches. We demonstrate that nonlinear time-based block layers and block layers that avoid information bottlenecks are most efficient to discriminate between natural and artificial oil spills. Compared with standard neural-networks which use single time frames, the integration of unordered temporal information increases the overall accuracy (OA) from 82% to 92% on our dataset, which demonstrates the effectiveness of the proposed approach.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Physical Knowledge-Enhanced Deep Neural Network for Sea Surface
           Temperature Prediction

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      Authors: Yuxin Meng;Feng Gao;Eric Rigall;Ran Dong;Junyu Dong;Qian Du;
      Pages: 1 - 13
      Abstract: Traditionally, numerical models have been deployed in oceanography studies to simulate ocean dynamics by representing physical equations. However, many factors pertaining to ocean dynamics seem to be ill-defined. We argue that transferring physical knowledge from observed data could further improve the accuracy of numerical models when predicting sea surface temperature (SST). Recently, the advances in Earth observation technologies have yielded a monumental growth of data. Consequently, it is imperative to explore ways to improve and supplement numerical models utilizing the ever-increasing amounts of historical observational data. To this end, we introduce a method for SST prediction that transfers physical knowledge from historical observations to numerical models. Specifically, we use a combination of an encoder and a generative adversarial network (GAN) to capture physical knowledge from the observed data. The numerical model data are then fed into the pretrained model to generate physics-enhanced data, which can then be used for SST prediction. Experimental results demonstrate that the proposed method considerably enhances SST prediction performance compared to several state-of-the-art baselines.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Remote Sensing of Particulate Organic Carbon on the Western Antarctic
           Peninsula Shelf Using a Color Index-Based Algorithm

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      Authors: Chengfeng Le;Ming Wu;Sunbin Cai;Na Liu;Jianfeng He;
      Pages: 1 - 13
      Abstract: A color index (CI)–based algorithm (CI $_{mathbf {POC}}$ ) for estimating particulate organic carbon (POC) concentrations from satellite observations is evaluated for the Western Antarctic Peninsula (WAP) shelf. The CI utilizes three spectral bands centered approximately at 490, 550, and 670 nm of the ocean color satellite sensors. The CI $_{mathbf {POC}}$ algorithm is calibrated and validated using field-measured POC concentration data and match-up satellite-derived remote-sensing reflectance ( $text{R}_{mathbf {rs}}$ ) data. For comparison, the performance of the blue–green (BG) band ratio algorithm is also evaluated using the same match-up dataset. Results show that the CI $_{mathbf {POC}}$ algorithm outperforms the BG band ratio algorithm in determining the POC concentration for the WAP shelf with good statistical parameters of algorithm performance for the CI $_{mathbf {POC}}$ algorithm. Given that the CI $_{mathbf {POC}}$ algorithm is built upon a three-band-difference concept and is less sensitive to artificial errors in $R_{mathbf {rs}}$ than the BG algorithm, CI $_{mathbf {POC}}$ can correct POC concentration by retrieving uncertainties caused by sea ice and cloud contaminations. Further evaluation showed that the CI $_{mathbf {POC}}$ algorithm can derive POC from the next-g-neration ocean color satellite sensors with robust performance and noise tolerance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Internal Wave Signature Extraction From SAR and Optical Satellite Imagery
           Based on Deep Learning

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      Authors: Shuangshang Zhang;Xiaofeng Li;Xudong Zhang;
      Pages: 1 - 16
      Abstract: Internal waves (IWs) are a common characteristic of oceans and serve a crucial role in transmitting energies between large-scale tides and small-scale mixing. This study developed a deep-learning-based method for extracting IW signatures on multiple satellite imagery from synthetic aperture radar (SAR) and optical sensors in sun-synchronous and geostationary orbits with varying spatial resolution. We collected 1115 satellite images, including 116 Environmental Satellite (ENVISAT) advanced SAR (ASAR), 839 MODerate-resolution Imaging Spectroradiometer (MODIS), and 160 Himawari-8 Advanced Himawari Imager (AHI) images with clear IW signatures in the South China Sea (SCS), Sulu Sea, and Celebes Sea for model training. Considering the distinct IW characteristics under different imaging mechanisms, the specially tailored IW extraction network (IWE-Net) leverages three modifications to improve the accuracy and robustness: online data augmentation, squeeze and excitation blocks, and Matthews correlation coefficient loss. The overall mean precision, recall, and F1-score of the IWE-Net model are 85.75%, 85.67%, and 85.71%, respectively, demonstrating that the model is accurate for IW signature extraction. We also proved the transferability of our method to sea areas worldwide, long-term periods, and Sentinel satellite sensors completely independent of the model training. Globally, the numbers of IW images and extracted pixels show an obvious tidal-related double-peak distribution. Furthermore, we processed 15461 MODIS images in the northeastern SCS to present a holistic IW distribution map over the past 22 years. An unreported IW silent zone caused by drastic topography changes has been discovered, indicating the great potential of deep learning in information retrieval from remote sensing imagery.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Ca-STANet: Spatiotemporal Attention Network for Chlorophyll-a Prediction
           With Gap-Filled Remote Sensing Data

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      Authors: Min Ye;Bohan Li;Jie Nie;Yuntao Qian;Lie-Liang Yang;
      Pages: 1 - 14
      Abstract: Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the monitoring data are subject to sparse sampling and difficult to be measured in a large-scale and synchronous way. Moreover, the advanced learning-based models for point Chl-a prediction, such as long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM, are unable to fully mine the spatiotemporal correlation of Chl-a variations. Therefore, by using the satellite remote sensing data with extensive coverage, we design a framework, namely, Ca-STANet, to simultaneously predict the Chl-a of all the locations in a large-scale area from the perspective of spatiotemporal field. Specifically, in our method, the original data are first divided into multiple subregions to capture the spatial heterogeneity of large-scale area. Then, two modules are, respectively, operated to mine the spatial correlation and long-term dependency features. Finally, the outputs from the two modules are integrated by a fusion module to fully mine the spatiotemporal correlations, which are exploited to attain the final Chl-a prediction. In this article, the proposed Ca-STANet is comprehensively evaluated and compared with the legacy methods based on the OC-CCI Chl-a 5.0 data of the Bohai Sea. The results demonstrate that the proposed Ca-STANet is highly effective for Chl-a prediction and achieves higher prediction accuracy than the baseline methods. Moreover, as the OC-CCI Chl-a 5.0 data have many missing areas, we introduce DINEOF method to fill the data gaps before using them for prediction.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Bayesian Algorithm for Rain Detection in Ku-Band Scatterometer Data

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      Authors: Ke Zhao;Ad Stoffelen;Jeroen Verspeek;Anton Verhoef;Chaofang Zhao;
      Pages: 1 - 16
      Abstract: Ku-band scatterometers are sensitive to rain effects due to their centimeter-scale radar wavelength. The NSCAT-4DS geophysical model function (GMF) corrects for sea surface temperature (SST), whereas it does not consider rain. Rain causes biases in the retrieved wind fields, and to prevent these, quality control (QC) flags play an important role in rain identification. Since horizontal polarization and vertical polarization radar beams have a particular sensitivity to rain clouds, a noticeable difference between the rain-dominated backscatter distribution and the wind-dominated backscatter distribution is observed. Employing a Bayesian approach and exploiting these particular wind and rain backscatter characteristics, we propose an algorithm to provide the posterior rain probability for each measurement in a wind vector cell and test the method for the Haiyang-2C scatterometer. In a comprehensive comparison between posterior rain probability, Royal Netherlands Meteorological Institute (KNMI) QC flag, and Joss flag, for posterior rain probabilities higher than 0.5, the rejection rate is approximately a quarter of that of the KNMI QC flag with better rain detection behavior. While the Joss flag, the difference between the retrieved wind speed and the 2-D variational ambiguity removal analysis wind speed, has the best performance in identifying rain in the sweet swath, it comes at the cost of a higher missing rate. The comparison with advanced scatterometer (ASCAT) winds also proves the method’s effectiveness. Posterior rain probability has the best rain identification ability in the nadir swath. A combination of different QC flags should be beneficial and applied in wind retrieval.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Coregistration of Hyperspectral Imagery With Photogrammetry for
           Shallow-Water Mapping

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      Authors: Håvard Snefjellå Løvås;Oliver Hasler;Dennis D. Langer;Asgeir J. Sørensen;
      Pages: 1 - 24
      Abstract: In this article, we present coregistration of hyperspectral imagery with photogrammetry for shallow-water mapping from an unmanned aerial vehicle (UAV). The coregistration is based on a methodology for georeferencing that utilizes the outputs from the photogrammetry pipeline through three steps. First, we perform the photogrammetry pipeline. This gives camera poses from structure-from-motion (SfM) and a dense point cloud from multiview stereo (MVS). Performing a refraction correction of the dense point cloud yields high-resolution bathymetry. Second, poses from SfM are fused with UAV navigation sensors in a Kalman smoother. Third, the geometric model of the hyperspectral imager (HSI) is calibrated to align hyperspectral images with photogrammetry. Then, ray tracing is performed to georeference spectral measurements onto the bathymetry from MVS. Using the georeferenced hyperspectral imagery, we present spectral bathymetry estimation. The methods were demonstrated for a coastal site (depth < 6 m) in Norway, using a UAV with a camera and an HSI. The georeferencing yielded a horizontal mean absolute error (MAE) of 22 cm between hyperspectral and photogrammetry, equivalent to one hyperspectral pixel. The MVS bathymetry gave an MAE of 14 cm with respect to ground-truth acoustics. The spectral bathymetry estimator was calibrated on ground-truth acoustics with an MAE of 10 cm. Comparing the bathymetry from MVS with the spectral bathymetry yielded an MAE of 11 cm. The results suggest that coregistration with photogrammetry yields accurate georeferencing of hyperspectral imagery. The results also show that we can map bathymetry accurately using MVS with refraction correction or the spectral estimator.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Conceptual Rain Effect Model for Ku-Band Scatterometers

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      Authors: Ke Zhao;Ad Stoffelen;Jeroen Verspeek;Anton Verhoef;Chaofang Zhao;
      Pages: 1 - 9
      Abstract: Satellite scatterometer wind retrieval is affected by rain. Both the precipitating clouds in the atmosphere and the sea surface rain effects can enhance or reduce the backscatter signal. Ku-band scatterometer retrievals suffer more rain effects than the C-band scatterometer due to the shorter wavelength. Because of the lack of understanding of the potential physical mechanism, the current geophysical model functions (GMFs) do not include rain effects, which leads to wind field retrieval biases in rainy areas. The usual method to avoid rain effects is flagging the possible rain-contaminated data in the quality control procedure and removing these flagged data in the processing. However, rain is often associated with extreme weather events, where accurate wind (and rain) retrieval is particularly relevant. Therefore, the authors propose a conceptual model that describes the relationship between Ku-band scatterometer-measured normalized radar cross-section (NRCS) biases and the sea surface wind-induced NRCS and rain rates (RRs). The model assumes that the area-weighted RR in each wind vector cell (WVC) is a function of the rain coverage area fraction. The received NRCS is constituted by a wind and rain contribution. Model parameters are fitted based on Haiyang-2C scatterometer measurements, collocated advanced scatterometer (ASCAT) measurements, and the Level 3 Integrated Multi-satellitE Retrievals (3IMERG) average area-weighted RRs. Scatterometer-measured NRCS biases are much reduced by comparing the original measured NRCS biases and the residual NRCS biases after correction. The model can help to better understand rain effects on scatterometers and paves the way toward a Ku-band scatterometer wind retrieval method considering rain effects.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Arctic Sea Ice and Open Water Classification From Spaceborne Fully
           Polarimetric Synthetic Aperture Radar

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      Authors: Yiru Lu;Biao Zhang;William Perrie;
      Pages: 1 - 13
      Abstract: We present an approach for automatic classification of Arctic sea ice and open water from spaceborne C-band RADARSAT-2 fully polarimetric (HH, HV, VH, VV) synthetic aperture radar (SAR) imagery based on the random forest (RF) model. The HH- and HV-polarized radar backscatter, incidence angle, and optimal polarimetric features are inputs of the RF model. First, we use a physics-based unsupervised scheme to generate well-annotated sea ice and open water samples. This scheme can alleviate labeling errors arising from visual interpretation or temporal–spatial mismatch between SAR images and operational ice charts. Subsequently, we estimate a set of characteristic polarimetric parameters and select the most representative features using the recursive feature elimination method. Finally, the RF model is trained and validated using more than one million labeled samples. Statistical validation results show that the overall sea ice and water classification accuracy is 99.94% and the Kappa coefficient is 0.999. We find that ice–water discrimination accuracy can be improved by about 4%–10% when optimal polarimetric features are involved in the RF model input. Moreover, the polarization difference (PD, VV–HH) is found to be the foremost polarimetric parameter for distinguishing sea ice from open water. The proposed approach has the capability of yielding satisfactory ice–water classification over the complicated marginal ice zone. High-resolution ice–water classification maps clearly show that sea ice leads and their surroundings are also well separated.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Tropical Cyclogenesis Detection From Remotely Sensed Sea Surface Winds
           Using Graphical and Statistical Features-Based Broad Learning System

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      Authors: Sheng Wang;Ka-Veng Yuen;Xiaofeng Yang;Yang Zhang;
      Pages: 1 - 15
      Abstract: This article proposed a graphical and statistical features-based broad learning system (GSF-BLS) to detect tropical cyclogenesis with the cross-calibrated multiplatform version 2.0 (CCMP V2.0) wind products. The framework of the proposed model is composed of three modules: the data preprocessing module, the feature extraction module, and the basis broad learning system (BLS). At the stage of data preprocessing, we use the CCMP V2.0 data to match the best tracks and the global tropical cloud cluster (TCC) tracks to obtain the developed and undeveloped samples. At the feature extraction stage, a convolution module with pretrained weights is used to extract the graphical features (GFs). Meanwhile, the statistical features (SFs) are calculated based on the divided subregions of each sample. Thus, the combination of these GFs and SFs forms the input vectors. Then, the training time of GSF-BLS on CPU is only 1/20th of that of deep learning models, showing its simplicity and efficiency in model training. The overall accuracy, probability of detection (POD), and false alarm rate (FAR) on the testing set are 89.46%, 86.78%, and 8.31%, respectively. More importantly, the incremental learning ability of GSF-BLS makes it superior to most deep learning models in model updating, which can avoid the computational burden caused by retraining. Finally, the case study results show that GSF-BLS can predict tropical cyclogenesis in 52 of 70 cases in advance, and the average lead times are 13.54 h. Therefore, the experimental results demonstrate that GSF-BLS is a promising tropical cyclogenesis detection model.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Rethinking BiSeNet: A Lightweight Network for Urban Water Extraction

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      Authors: Peng Nie;Xi Cheng;Zeyi Song;Mingqiu Mao;Tingting Wang;Likang Meng;
      Pages: 1 - 10
      Abstract: Urban water runs through and plays an important role in urban development, and understanding the spatial changes of urban water bodies is significant. However, the rapid growth of remote sensing data caused by rapid urbanization poses a challenge to the efficiency of water extraction methods. In this article, a high-precision method with a lightweight structure for urban water extraction was proposed in terms of the dilemma between efficiency and accuracy. We designed a squeeze-and-excitation attention refined module (SE-ARM), squeeze-and-excitation feature fusion module (SE-FFM), and depth-wise atrous spatial pyramid pooling (DW-ASPP) based on BiSeNet to improve it in the water semantic segmentation application, which resulted in SE-BiSeNet. To validate the proposed model’s effectiveness, we compared our SE-BiSeNet to other advanced water extraction methods on the Chengdu dataset in terms of accuracy and efficiency. The results showed that the proposed model performs well in both accuracy [0.96 intersection over union (IoU)] and efficiency (6.4 frame/s on a GTX 1060 card) with fewer parameters and calculations, which indicates an objective application potential.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Sea Ice Classification Using Mutually Guided Contexts

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      Authors: Xiaoyu Sun;Xi Zhang;Weimin Huang;Zongjun Han;Xinrong Lyu;Peng Ren;
      Pages: 1 - 19
      Abstract: In this article, sea ice classification on a remote sensing image given just a small number of labeled pixels is investigated. Effective sea ice classification is rendered from two aspects. First, a feature extraction method is developed. It extracts the context feature from a classification map. Second, an iterative learning paradigm is established. The labeled pixels are divided into two training subsets. At each iteration, the context feature for one subset is extracted from the classification map which is obtained subject to the other subset. Therefore, the two subsets mutually guide each other in updating the context feature in an iterative manner, which finally renders effective sea ice classification. The above-mentioned paradigm is referred to as mutually guided contexts. The advantages of the new paradigm are twofold. First, the context feature enriches the sea ice image representation in a general manner regardless of the types of raw image data. Second, the two training subsets keep providing different refined classification maps for each other such that the comprehensiveness of the context feature is recursively enhanced. Therefore, the paradigm of mutually guided contexts comprehensively characterizes the sea ice image representation for training and classification even when only small training data are available. Experiments validate the effectiveness of the mutually guided contexts for sea ice classification.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • High Spatial Resolution Gap-Free Global and Regional Ocean Color Products

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      Authors: Xiaoming Liu;Menghua Wang;
      Pages: 1 - 18
      Abstract: Satellite ocean color products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration (NOAA)-20, and the Ocean and Land Colour Instrument (OLCI) on the Sentinel-3A (S3A) and Sentinel-3B (S3B) have been widely used for surveillance of the ocean environment and research on ocean physical, biological, biogeochemical, and ecological processes. However, either VIIRS or OLCI daily ocean color images are often incomplete in spatial coverage due to cloud cover, contamination of high sun glint, narrow swath width, high sensor-zenith angle, high solar-zenith angle, and/or other unfavorable retrieval conditions. Although merging daily ocean color images from multiple satellite sensors can help reduce the number of invalid pixels, gap-filling methods, such as the data interpolating empirical orthogonal function (DINEOF), are often used to reconstruct invalid pixels and generate gap-free images. The 9-km spatial resolution global gap-free ocean color data have been routinely produced by the NOAA Ocean Color Science Team and distributed through NOAA CoastWatch (https://coastwatch.noaa.gov/cw/index.html). In this study, we aim to develop and produce improved spatial resolution gap-free products, including chlorophyll-a (Chl-a) concentration, diffuse attenuation coefficient at the wavelength of 490 nm [ $K_{d}$ (490)], and suspended particulate matter (SPM) concentration for spatial resolutions of 0.5-, 1-, and 2-km. Two-sensor (VIIRS-SNPP and VIIRS-NOAA-20), three-sensor (two-sensor + OLCI-S3A), and four-sensor (three-sensor + OLCI-S3B) daily merged global Level-3 ocean color data are created and compared. It is found that, by merging data from the two VIIRS sensors, ~38% more valid ocean product d-ta are retrieved compared with a single sensor from either SNPP or NOAA-20. Adding OLCI-S3A to the two-sensor merged data can increase the number of valid pixels by ~12%, and adding OLCI-S3B to the three-sensor merged data can further increase the number of valid pixels by ~8%. The DINEOF method is applied to daily two-, three-, and four-sensor merged data to generate global 2-km resolution gap-free images. Results show that 2-km resolution gap-free images are able to resolve fine ocean features, such as coastal eddies and filaments, which are not available in the 9-km resolution images. While adding OLCI-S3A data significantly improves the three-sensor-derived gap-free images over the two-sensor images, no significant enhancement is found in the four-sensor-derived gap-free images by adding OLCI-S3B data. The DINEOF method is also applied to 1- and 0.5-km resolution four-sensor merged Chl-a, $K_{d}$ (490), and SPM images in the Gulf of Mexico and U.S. West Coast region. It is found that both 0.5- and 1-km resolution images show more detailed ocean structures and features in coastal regions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multimodal 4DVarNets for the Reconstruction of Sea Surface Dynamics From
           SST-SSH Synergies

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      Authors: Ronan Fablet;Quentin Febvre;Bertrand Chapron;
      Pages: 1 - 14
      Abstract: The space-time reconstruction of sea surface dynamics from satellite observations is a challenging inverse problem due to the associated irregular sampling. Satellite altimetry provides direct observation of the sea surface height (SSH), which relates to the divergence-free component of sea surface currents. The associated sampling pattern prevents operational schemes from retrieving fine-scale dynamics, typically below 10 days. By contrast, other satellite sensors provide higher-resolution observations of sea surface tracers such as sea surface temperature (SST). Multimodal inversion schemes then arise as appealing approaches. Though theoretical evidence supports the existence of an explicit relationship between SST and sea surface dynamics under specific dynamical regimes, the generalization to the variety of upper ocean dynamical regimes is complex. Here, we investigate this issue from a physics-informed learning perspective. We introduce a trainable multimodal inversion scheme for the reconstruction of sea surface dynamics from multisource satellite-derived observations, namely satellite-derived SSH and SST data. The proposed multimodal 4DVarNet schemes combine a variational formulation involving trainable observation and a priori terms with a trainable gradient-based solver. An observing system simulation experiment (OSSE) for a Gulf stream region supports the relevance of our approach compared with state-of-the-art schemes. We report a relative improvement greater than 60% compared with the operational altimetry product in terms of root mean square error (MSE) and resolved space-time scales. We discuss further the potential and the limitations of the proposed approach for the reconstruction and forecasting of geophysical dynamics from irregularly-sampled satellite observations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Lightweight Neural Network for Spatiotemporal Filling of Data Gaps in Sea
           Surface Temperature Images

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      Authors: Stephanie Baker;Zhi Huang;Bronson Philippa;
      Pages: 1 - 10
      Abstract: Optical remotely sensed data often have data gaps due to cloud coverage, which hinders their full potential in many environmental applications. The question of how to accurately and effectively reconstruct the measurements in the data gaps remains a challenge. In this study, we developed a bidirectional long short-term memory (BiLSTM) model with a novel and adaptable custom temporal penalty layer for spatiotemporal gap filling. The model was trained and tested in time-series daily nighttime sea surface temperature (SST) images acquired by the Himawari-8 satellite. The modeling results showed strong performance, accurately reconstructing spatial and temporal features of cloud-affected SST data. Our neural network achieved a per-image MAE of 0.1193 °C and per-image root mean square error (RMSE) of 0.1293 °C. The model was also able to produce realistic SST time-series predictions that were consistent with the expected seasonal variables. Importantly, the BiLSTM model outperformed the previous state-of-the-art simple spatial gap-filling processor (SSGP) algorithm in terms of error metrics, structural similarity (SSIM), and computational efficiency. Overall, the results of this study indicate that the BiLSTM neural network model we developed is highly suitable for spatiotemporal gap filling of SST and other remotely sensed data.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Ocean Salinity Retrieval and Prediction for Soil Moisture Active Passive
           Satellite Using Data-to-Data Translation

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      Authors: Kyung-Hoon Han;Sungwook Hong;
      Pages: 1 - 15
      Abstract: Sea surface salinity (SSS) is a key parameter in physical oceanography, global hydrological, biochemical cycles, and climate change studies. L-band radiometers onboard satellites, such as soil moisture active passive (SMAP), soil moisture, and ocean salinity (SMOS), have played a crucial role in monitoring and analyzing the global SSS in the past decades. This study presents a method to retrieve and predict the SMAP SSS through data-to-data translation (D2D) based on conditional generative adversarial networks (CGANs). The model was constructed from the polarized brightness temperatures (TBs), differences in the polarized TBs from the L-band of the SMAP satellite, and sea surface temperature (SST) and sea surface wind speed (SSW) from the European Center for Medium-Range Weather Forecasts data from April 2015 to July 2020, and applied to produce SMAP SSS. A comparison between the SMAP salinity and the D2D-generated SMAP salinity showed excellent agreement, evaluated through bias = 0.016, root mean square error (RMSE) = 0.173 in practical salinity unit (psu), and correlation coefficient (CC) = 0.985. Furthermore, a comparison between the D2D-generated and buoy-observed salinities showed good agreement (bias = −0.031 psu and RMSE = 0.196 psu, CC = 0.971). In addition, the results of the one-month prediction model were also in good agreement with the SMAP SSS (bias = 0.028 psu and RMSE = 0.218 psu, CC = 0.977). Consequently, the D2D-based model can be effectively used to generate SMAP SSS information and can be applied to various microwave satellites.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Appraisal of Atmospheric Correction and Inversion Algorithms for
           Mapping High-Resolution Bathymetry Over Coral Reef Waters

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      Authors: Yuye Huang;Hongqiang Yang;Shilin Tang;Yongming Liu;Yupeng Liu;
      Pages: 1 - 11
      Abstract: Bathymetric inversions constitute a key preliminary step when characterizing coral topography. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is poised to provide true values for satellite-derived bathymetry (SDB) in the absence of measured data. Nevertheless, the influences of the different available algorithms and atmospheric correction (AC) processes applied to construct SDB over coral reef waters are still poorly understood. In this article, three empirical-based bathymetric algorithms [the linear ratio model (LRM), polynomial ratio model (PRM), and exponential ratio model (ERM)] and three ACs [AC for the operational land imager (OLI) lite (ACOLITE), sea-viewing wide field-of-view sensor (SeaWiFS) data analysis system (SeaDAS), and sentinel 2 (atmospheric) correction (Sen2Cor)] are evaluated with Sentinel-2A/B multispectral imagery to achieve fine-spatial-resolution bathymetric information. The bathymetry estimates produced by fusing the Sentinel-2A/B and ICESat-2 datasets using the three models exhibit sufficient accuracies, with low percent errors and mean absolute errors (MAEs) between 0.44 and 0.74 m and root-mean-square errors (RMSEs) between 0.61 and 0.96 m. In all cases, the PRM outperforms the ERM and LRM. The AC method comparisons show that the performance of ACOLITE is better than those of Sen2Cor and SeaDAS. The obtained depth estimates are less sensitive to the impacts of the AC methods in relatively shallow waters than in relatively deep waters, and ACOLITE performs more stably. Overall, the PRM coupled with the ACOLITE method performs best. With depths extracted from ICESat-2, high-spatial-resolution SDB data capable of supporting the long-range goal of coral topography analyses are attainable even without in situ datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Spectral–Spatial Depth-Based Framework for Hyperspectral Underwater
           Target Detection

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      Authors: Qi Li;Jinghua Li;Tong Li;Zheyong Li;Pei Zhang;
      Pages: 1 - 15
      Abstract: Ocean-related research is of critical significance to the national marine military force. Hyperspectral underwater target detection (HUTD) has attracted widespread attention in recent years. However, most of the previous methods only relied on the spectral features of underwater targets and did not fully exploit their spatial characteristics. To address the issue, a spectral–spatial depth-based framework is proposed, which utilizes the 3-D convolution operation to capture spectral–spatial features and gains finer detection based on predicted depth. In particular, the proposed framework adopts the data transferring network to remove the noise interference by transferring the real hyperspectral data into corresponding synthetic data, which are exploited to train models. Then, considering that underwater target spectra highly depend on its depth in water, the depth estimation network is utilized to predict an accurate depth of a target, which can contribute to selecting a suitable detection network and gaining a general contour of the target. Since the underwater target spectrum is jointly determined by the target and the surrounding water column, the spectral–spatial detection network extracts the spectral–spatial features for underwater target detection. Using a pool dataset, sea dataset, and synthetic hyperspectral image (HSI), we evaluate the performance of the proposed framework in terms of receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) value, both qualitatively and quantitatively. Meanwhile, extensive detection experiments demonstrate the robustness and effectiveness of the TDSS-UTD over several state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Direction-of-Arrival Estimation for Nested Acoustic Vector-Sensor Arrays
           Using Quaternions

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      Authors: Yi Lou;Xinghao Qu;Dawei Wang;Julian Cheng;
      Pages: 1 - 14
      Abstract: There is an increasing interest in direction-of-arrival (DOA) estimation using nested arrays composed of vector sensors. Considering acoustic vector sensors (AVSs) commonly used in underwater applications, this article proposes a novel algorithm, called augmented nested quaternion-multiple signal classification (ANQ-MUSIC), to perform DOA estimation. By judiciously arranging the multicomponent outputs of AVSs, we model the received signals from the entire nested AVS array (NAA) as a quaternion observation vector in a compact way to reduce computational complexity. Next, we formulate a quaternion-based difference co-array (QDCA) model via vectorizing the quaternion covariance matrix (QCM). Based on the obtained insights from the QDCA model, we derive a suitable QCM, which is constructed by applying the spatial smoothing (SS) technique. Finally, classical quaternion-MUSIC (Q-MUSIC) is logically introduced to estimate the DOA parameters. In simulations, we take into account nonuniform received noise and intercomponent correlated (ICC) noise, which may occur in practical underwater environments. The results demonstrate that the proposed method shows superiority in angular resolution and achieves a desirable tradeoff between estimate accuracy and computational burden, besides showing robust performance in the above test scenarios.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Toward the Discrimination of Oil Spills in Newly Formed Sea Ice Using
           C-Band Radar Polarimetric Parameters

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      Authors: Elvis Asihene;Gary Stern;David G. Barber;Colin Gilmore;Dustin Isleifson;
      Pages: 1 - 15
      Abstract: Climate-driven sea ice loss has exposed the Arctic to increased human activity, which comes along with a higher risk of oil spills. As a result, we investigated the ability of C-band polarimetric parameters in a controlled mesocosm to accurately identify and discriminate between oil-contaminated and uncontaminated newly formed sea ice (NI). Parameters, such as total power, copolarization ratio, copolarization correlation coefficient, and others, were derived from the normalized radar cross section and covariance matrix to characterize the temporal evolution of NI before and after oil spill events. For separation purposes, entropy ( $H$ ) and mean-alpha ( $alpha$ ) were extracted from eigen decomposition of the coherency matrix. The $H$ versus $alpha $ scatterplot revealed that a threshold classifier of 0.3- $H$ and 18°- $alpha $ could distinguish oil-contaminated NI from its oil-free surroundings. From the temporal evolution of the polarimetric parameters, the results demonstrate that the copolarization correlation coefficient is the most reliable polarimetric parameter for oil spill detection, as it provides information on a variety of oil spill scenarios, including oil encapsulated within ice and oil spreading on top of ice. Overall, these findings will be used to support existing and future C-band polarimetric radar satellites for resolving ambiguities associated with Arctic oil spill events, particularly during freeze-up seasons.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Predicting Sea Ice Concentration With Uncertainty Quantification Using
           Passive Microwave and Reanalysis Data: A Case Study in Baffin Bay

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      Authors: Xinwei Chen;Ray Valencia;Armina Soleymani;K. Andrea Scott;
      Pages: 1 - 13
      Abstract: In recent years, the adoption of deep learning (DL) techniques for predicting sea ice concentration (SIC) given both passive microwave (PM) data and reanalysis data has seen a growing interest. For use in downstream services, these SIC estimates should be accompanied by uncertainty estimates. To provide these estimates, we utilize a heteroscedastic Bayesian neural network (HBNN), which can estimate both model (epistemic) and data (aleatoric) uncertainty. We use both PM and atmospheric data as our input features and demonstrate that both are needed for accurate SIC estimates. Results show that, over an annual cycle, the months of melt onset, such as April, May, and June, produce the highest uncertainties relative to other months, with total (epistemic + aleatoric) uncertainties of approximately 20%, while areas in the marginal ice zone contributed highest total uncertainty of 25% spatially. When considering an average over the test year, the level of uncertainty due to the data (aleatoric) is consistent with other studies, at 10%–15%. The advantage of our approach is that the uncertainties are specific to the data instance, and both model and data uncertainties are estimated.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Joint Active and Passive Microwave Thermometry of Ice Sheets

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      Authors: Anna L. Broome;Dustin M. Schroeder;Joel T. Johnson;
      Pages: 1 - 10
      Abstract: Measurement of ice-sheet thermal state via microwave remote sensing techniques has the potential to provide critically needed observations on englacial temperature at the local and continental scale. Better constraints of the vertical englacial temperature structure are needed to improve understanding of thermomechanical processes, ice rheology, and basal sliding, and to reduce uncertainty in interpretations of basal conditions such as material, roughness, and thermal state. We investigate the potential to combine active and passive microwave remote sensing techniques, namely ice-penetrating radar sounding and microwave radiometry, to enable more precise, accurate, and robust measurement of ice-sheet thermal state across widely varying thermal regimes. We simulate the effects of englacial temperature profiles on the attenuation and brightness temperature and explore the performance tradeoffs for joint radar-radiometer system architectures. Our analysis shows that active and passive microwave measurements have complementary sensitivities to englacial temperature as a function of depth, and that a ground-based joint radar-radiometer system can reduce the requirements and complexity demanded of each single instrument.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Sea Ice Concentration Retrieval Algorithm Based on Relationship
           Between AMSR2 89-GHz Polarization and Landsat 8 Observations

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      Authors: Yasuhiro Tanaka;Junshen Lu;
      Pages: 1 - 15
      Abstract: A newly developed linear sea ice concentration (SIC) retrieval algorithm based on passive microwave Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements is proposed. SIC is retrieved by a linear function of the polarization ratio (PR) at 89 GHz (PR $_{mathbf {89}}$ ) corrected for atmospheric influence. We use Landsat 8 SIC data to derive the coefficients of the linear function. Results using this linear algorithm are compared to those of ASI2 developed by Lu et al. (2018), which is a nonlinear 89-GHz algorithm with polarization difference (PD) at 89 GHz (PD $_{mathbf {89}}$ ) that also includes a correction for atmospheric influence. Both algorithms are compared with independent SIC data derived from Landsat 8, ship-based observation, and synthetic aperture radar (SAR) and both tend to underestimate the ship-based and Landsat 8 SICs, particularly over thin ice. However, the proposed algorithm tends to provide results with lower bias and root-mean-square error (RMSE) for different ice categories.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Optimization of Cross Correlation Algorithm for Annual Mapping of Alpine
           Glacier Flow Velocities; Application to Sentinel-2

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      Authors: Jérémie Mouginot;Antoine Rabatel;Etienne Ducasse;Romain Millan;
      Pages: 1 - 12
      Abstract: Nowadays, satellite observations cover most of the Earth’s surface in a repetitive manner. This information is crucial for documenting variability and environmental changes such as glacier surface velocity. With this in mind, digital image processing has been developed and improved over the past decades. The processing challenges are now related to optimizing parameters that account for the high variability of natural processes, as well as filtering and aggregating the results to provide useful products to end-users. Based on the normalized cross correlation (NCC) method applied to Sentinel-2 optical satellite observations up to 400 days apart, we present a series of tests to derive optimal parameter values for the quantification of alpine glacier ice velocity that we have applied to the Mont-Blanc massif where in situ measurements are available. We found that a search distance adapted to the temporal baseline, a $16times 16$ pixel window size, and a $5times 5$ pixels sampling provide an appropriate combination of parameters to process Sentinel-2 with the NCC method when applied to small alpine glaciers. Combining several spatial and temporal filters applied to a large set of more than 18000 displacement maps obtained between 2015 and 2021, then aggregating these filtered maps using statistical or linear regressions into annual maps, yields near-complete maps of the test region with a root mean square error (RMSE) reduced to about 10 m.yr $^{-1}$ compared to in situ measurements.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Unsupervised Snow Segmentation Approach Based on Dual-Polarized
           Scattering Mechanism and Deep Neural Network

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      Authors: Chang Liu;Zhen Li;Zhipeng Wu;Lei Huang;Ping Zhang;Gang Li;
      Pages: 1 - 14
      Abstract: Distribution of snow and its melting is a critical factor affecting local weather, avalanche and flood forecasting, livelihood of people residing, and hydropower production. Most of the existing dry and wet snow identification methods were based on expensive quad-pol synthetic aperture radar (SAR) with finite generalizability, while dual-pol SAR with larger coverage, longer time series, and open availability has more advantages. In this study, an unsupervised algorithm for dry and wet snow discrimination, neighborhood-based sparse autoencoder (NSAE)-weighted fuzzy C-means clustering (WFCM), is proposed based on a variety of polarimetric features derived from the $H$ – $alpha $ decomposition in the dual-pol mode using the C-band Sentinel-1 SAR data. NSAE-WFCM constructs a deep training network using the pixel NSAE to optimize polarimetric parameters and inputs reconstructed features with different weights into feature-WFCM to distinguish dry and wet snow for each underlying surface. Ground observation was carried out during the snow melting period of March 2021 in Altay, China, to validate the dual-pol NSAE-WFCM method with an overall accuracy and a Kappa coefficient of 88.8% and 0.68, respectively. The results show that NSAE-WFCM’s accuracy is similar to that of the quad-pol SAR-based dry and wet snow result (90.0%) and significantly better than that of previously published approaches extended to dual-pol SAR, such as support vector machine (SVM) (76.7%), H– $alpha $ -Wishart (65.5%), total power-based method (51.7%), and wet snow-based method (43.1%). Therefore, the NSAE-WFCM algorit-m improves the ability to classify wet and dry snow based on dual-pol polarimetric features, overcomes the high dependence of existing methods on quad-pol SAR data, and reduces manual interpretation by using unsupervised clustering.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk
           Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR
           Measurements

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      Authors: Hoyeon Shi;Sang-Moo Lee;Byung-Ju Sohn;Albin J. Gasiewski;Walter N. Meier;Gorm Dybkjær;Sang-Woo Kim;
      Pages: 1 - 18
      Abstract: Information on snow depth on sea ice and bulk sea ice density is required to convert CryoSat-2 radar freeboard ( $F_{r}$ ) into sea ice thickness (SIT). It is difficult to obtain their information on an Arctic basin scale; therefore, most CryoSat-2 SIT products largely rely on the distributions of snow depth and bulk sea ice density derived from parameterizations, which are based on sea ice type and climatological values. Several observational studies have found that the distributions of parameterized variables are inaccurate compared to the actual distributions. This study aims to develop a new type of retrieval algorithm for snow depth, SIT and bulk density, and ice freeboard in the Arctic winter by synergizing active CryoSat-2 with passive microwave and infrared measurements. Two parameterizations for the snow–ice thickness ratio and bulk sea ice density were combined with the hydrostatic balance and radar wave speed correction equations. Consequently, solutions for the four target variables were obtained and applied to different CryoSat- $2~F_{r}$ , derived from empirical and waveform-fitting (WF) retracker algorithms. The retrieved thickness-related parameters based on $F_{r}$ from the Lognormal WF retracker algorithm showed good agreement with the airborne snow depth, total freeboard, and mooring ice draft measurements. The retrieved multiyear sea ice bulk density was significantly higher than the value of 882 kg $cdot ~text{m}^{-3}$ , which was used in the previous density parameterization, showing a higher agreement with values from in situ measurements. The spatial and interannual variabilities of SIT increased when the results from this stud- were compared with those based on previous parameterizations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Wintertime Polynya Structure and Variability From Thermal Remote Sensing
           and Seal-Borne Observations at Pine Island Glacier, West Antarctica

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      Authors: Elena Savidge;Tasha Snow;Matthew R. Siegfried;Yixi Zheng;Ana B. Villas Bôas;Guilherme A. Bortolotto;Lars Boehme;Karen E. Alley;
      Pages: 1 - 13
      Abstract: Antarctica’s ice shelves play a critical role in modulating ice loss to the ocean by buttressing grounded ice upstream. With the potential to impact ice-shelf stability, persistent polynyas (open-water areas surrounded by sea ice that occur across multiple years at the same location) at the edge of many ice-shelf fronts are maintained by winds and/or ocean heat and are locations of strong ice–ocean–atmosphere interactions. However, in situ observations of polynyas are sparse due to the logistical constraints of collecting Antarctic field measurements. Here, we used wintertime (May–August) temperature and salinity observations derived from seal-borne instruments deployed in 2014, 2019, and 2020, in conjunction with thermal imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Landsat 8 Thermal Infrared Sensor (TIRS) to investigate spatial, temporal, and thermal structural variability of polynyas near Pine Island Glacier (PIG). Across the three winters considered, there were 176 anomalously warm ( $3sigma $ from background) seal dives near the PIG ice front, including 26 dives that coincided with MODIS images with minimal cloud cover that also showed a warm surface temperature anomaly. These warm surface temperatures correlated with ocean temperatures down to 150 m depth or deeper, depending on the year, suggesting that MODIS-derived surface thermal anomalies can be used for monitoring polynya presence and structure during polar night. The finer spatial resolution (100 m) of TIRS wintertime thermal imagery captures more detailed thermal structural variability within these polynyas, which may provide year-round insight into subice-shelf processes if this dataset is collected operationally.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Automated Detection and Depth Determination of Melt Ponds on Sea Ice in
           ICESat-2 ATLAS Data—The Density-Dimension Algorithm for Bifurcating
           Sea-Ice Reflectors (DDA-Bifurcate-Seaice)

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      Authors: Ute Christina Herzfeld;Thomas M. Trantow;Huilin Han;Ellen Buckley;Sinéad Louise Farrell;Matthew Lawson;
      Pages: 1 - 22
      Abstract: As climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover is imminent, understanding melt ponding is central to understanding changes in the new Arctic. National Aeronautics and Space Administration (NASA)’s Ice, Cloud and land Elevation Satellite (ICESat-2) has the capacity to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression. Yet ponds are currently not identified on the ICESat-2 standard sea-ice products, in which only a single surface is determined. The objective of this article is to introduce a mathematical algorithm that facilitates automated detection of melt ponds in the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) data, retrieval of two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds. With ATLAS, ICESat-2 carries the first spaceborne multibeam micropulse photon-counting laser altimeter system, operating at 532-nm frequency. ATLAS data are recorded as clouds of discrete photon points. The Density-Dimension Algorithm for bifurcating sea-ice reflectors (DDA-bifurcate-seaice) is an autoadaptive algorithm that solves the problem of pond detection near the 0.7-m nominal along-track spacing of ATLAS data, utilizing the radial basis function for calculation of a density field and a threshold function that automatically adapts to changes in the background, apparent surface reflectance, and some instrument effects. The DDA-bifurcate-seaice is applied to large ICESat-2 datasets from the 2019 and 2020 melt seasons in the multiyear Arctic sea-ice region. Results are evaluated by comparison with those from a manually forced algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Detection of Antarctic Surface Meltwater Using Sentinel-2 Remote Sensing
           Images via U-Net With Attention Blocks: A Case Study Over the Amery Ice
           Shelf

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      Authors: Lihang Niu;Xueyuan Tang;Shuhu Yang;Yun Zhang;Lei Zheng;Lijuan Wang;
      Pages: 1 - 13
      Abstract: Surface meltwater critically impacts the Antarctic mass balance and global sea level rise. Quantifying the extent of surface meltwater in Antarctica on a large scale is a challenging task. Traditional methods, such as thresholding, have many limitations. We used a deep learning method, the U-Net with attention blocks, to automatically extract surface meltwater from Sentinel-2 images. We inserted attention mechanism blocks into U-Net to assign different weights to all pixels and channels to utilize the high resolution and multiple channels of Sentinel-2 images. The model was used to map surface water bodies in Sentinel-2 images, and the average accuracy reached 0.9969 on the test dataset. In East Antarctica, the Amery ice shelf (AIS) exhibits the largest surface meltwater area. Studying surface meltwater dynamics on the AIS is useful for understanding the East Antarctic mass balance and demonstrating the model performance. We analyzed the classification results for surface water bodies on the AIS from January 2017 to 2022. Spatially, 96% of surface water bodies are concentrated inland of the AIS from 70° S to 73° S and account for 93% of the region 20 km from the coastline of the AIS. Temporally, the water body area varies considerably in different years, with a maximum in 2017 (932.54 km $^{2}$ ) and a minimum in 2021 (58.34 km $^{2}$ ). The spatial distribution of surface water bodies on the AIS is controlled by the firn air content (FAC), katabatic winds, bare rocks, and blue ice. The interannual variability is associated with complex climate factors, including temperature, surface net solar radiation, snowfall, and snowmelt, among which temperature and snowfall show a strong correlation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat:
           An Ecological Zoning Random Forest Approach

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      Authors: Zhaocong Wu;Fanglin Shi;
      Pages: 1 - 16
      Abstract: Forest canopy height (FCH) is a crucial indicator in the calculation of forest biomass and carbon sinks. There are various methods to measure FCH, such as space-borne light detection and ranging (LiDAR), but their data are spatially discrete and do not provide continuous FCH maps. Therefore, an FCH estimation method that associates sparse LiDAR data with spatially continuous variables is required. The traditional approach of constructing a single model overlooks the spatial variability in forest growth, which will limit the FCH accuracy. Considering the distinct nature of forest in different ecological zones, the following hold. First, we proposed an ecological zoning random forest (EZRF) model in 33 ecological zones in China. Compared with the total zone RF (TZRF) model, the EZRF model showed a greater potential, which was 21.5%–36.5% more accurate than the TZRF model. Second, we analyzed a total of 62 variables related to forest growth, including Landsat variables and ancillary variables (forest canopy cover, bioclimatic, topographic, and hillshade factors). An insight into variable selection in FCH modeling was provided by analyzing the prediction accuracy of FCH under different categorical variables and analyzing the importance of variables in different ecological zones. Third, finally, we produced a 30-m continuous FCH map by the EZRF model. Compared with the airborne LiDAR data, the FCH prediction results produced a root mean square error (RMSE) of 2.50–5.35 m, which were 41%–72% more precisely than the Global FCH, 2019. The results demonstrate the effectiveness of our proposed method and contribute to the study of forest carbon sinks.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Land Cover Classification With Gaussian Processes Using
           Spatio-Spectro-Temporal Features

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      Authors: Valentine Bellet;Mathieu Fauvel;Jordi Inglada;
      Pages: 1 - 21
      Abstract: In this article, we propose an approach based on Gaussian processes (GPs) for large-scale land cover pixel-based classification with Sentinel-2 satellite image time series (SITS). We used a sparse approximation of the posterior combined with variational inference to learn the GP’s parameters. We applied stochastic gradient descent and GPU computing to optimize our GP models on massive datasets. The proposed GP model can be trained with hundreds of thousands of samples, compared to a few thousands for traditional GP methods. Moreover, we included the spatial information by adding the geographic coordinates into the GP’s covariance function to efficiently exploit the spatio-spectro-temporal structure of the SITS. We ran experiments with Sentinel-2 SITS of the full year 2018 over an area of 200000 km2 (about 2 billion pixels) in the south of France, which is representative of an operational setting. Adding the spatial information significantly improved the results in terms of classification accuracy. With spatial information, GP models have an overall accuracy of 79.8. They are more than three points above random forest (the method used for current operational systems) and more than one point above a multilayer perceptron. Compared to a transformer-based model (which provides state-of-the-art results in the literature, but is not applied in operational systems), GP models are only one point below.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Method for Retrieving Coarse-Resolution Leaf Area Index for Mixed Biomes
           Using a Mixed-Pixel Correction Factor

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      Authors: Yadong Dong;Jing Li;Ziti Jiao;Qinhuo Liu;Jing Zhao;Baodong Xu;Hu Zhang;Zhaoxing Zhang;Chang Liu;Yuri Knyazikhin;Ranga B. Myneni;
      Pages: 1 - 17
      Abstract: The leaf area index (LAI) is a key structural parameter of vegetation canopies. Accordingly, several moderate-resolution global LAI products have been produced and widely used in the field of remote sensing. However, the accuracy of the current moderate-resolution global LAI products cannot satisfy the requirements recommended by the LAI application communities, especially in heterogeneous areas composed of mixed land cover types. In this study, we propose a mixed-pixel correction (MPC) method to improve the accuracy of LAI retrievals over heterogeneous areas by considering the influence of heterogeneity caused by the mixture of different biome types with the help of high-resolution land cover maps. The DART-simulated LAI, the aggregated Landsat LAI, and the site-based high-resolution LAI reference maps are used to evaluate the performance of the MPC method. The results indicate that the MPC method can reduce the influences of spatial heterogeneity and biome misclassification to obtain the LAI with much better accuracy than the Moderate Resolution Imaging Spectroradiometer (MODIS) main algorithm, given that the high-resolution land cover map is accurate. The root mean square error (RMSE) (bias) decreases from 0.749 (0.486) to 0.414 (0.087), while the R2 increases from 0.084 to 0.524, and the proportion of pixels that fulfill the uncertainty requirement of the GCOS increases from 38.2% to 84.6% for the results of site-based high-resolution LAI reference maps. Spatially explicit information about vegetation fractional cover can further reduce uncertainties induced by variations in canopy density for the results of DART simulated data. The proposed method shows potential for improving global moderate-resolution LAI products.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Passive Only Microwave Soil Moisture Retrieval in Indian Cropping
           Conditions: Model Parameterization and Validation

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      Authors: Dileep Kumar Gupta;Prashant K. Srivastava;Dharmendra Kumar Pandey;Sumit Kumar Chaudhary;Rajendra Prasad;Peggy E. O’Neill;
      Pages: 1 - 12
      Abstract: The present study carried out to parameterize the single channel soil moisture active passive (SMAP) passive soil moisture (SM) retrieval algorithm, over Indian conditions. The moderate resolution imaging spectroradiometer (MODIS) data products and soil texture data were used for an improved parameterization of the algorithm. The bias correction was applied to the MODIS leaf area index (LAI) for accurate computation of vegetation optical depth. The necessary vegetation and roughness parameter were calibrated through minimization of the error between model retrieved and ground measured SM. The value of root mean square error (RMSE) for retrieved SM was found as $0.059,,m^{3}m^{-3}$ with bias and correlation coefficients of $0.036,,m^{3}m^{-3}$ and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE = $0.059,,m^{3}m^{-3}$ , bias = $0.024,,m^{3}m^{-3}$ , and correlation coefficients = 0.752) for descending overpass. The same method is also implemented on two other test sites in different regions of India to check the model robustness, which indicates that the current parameterization provides a better estimate of SM over croplands in India. The overall performance of new parameterized model is found as (RMSE = 0.052 and bias = 0.034) for ascending and descending (RMSE = 0.048 and bias = 0.026) satellite overpasses for all the three test sites. Additionally, the intercomparing of various operational SM products SMAP SM (L2_SM_P), Soil Moisture and Ocean Salinity (SMOS) SM (SMOS&-x005F;L3_SM), and SMOS-IC data products was carried out with the SAC-ISRO PAN India SM network, which showed a significant RMSE, dry and wet biases over all three test sites as compared to the developed improved parameterized algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Determination and Evaluation of Surface Solar Irradiance With the
           MAGIC-Heliosat Method Adapted to MTSAT-2/Imager and Himawari-8/AHI Sensors
           

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      Authors: Enric Valor;Jesús Puchades;Raquel Niclòs;Joan Miquel Galve Romero;Oriol Lacave;Patrícia Puig;
      Pages: 1 - 19
      Abstract: Surface solar irradiance (SSI) is a crucial component of the radiation budget at the surface, which governs water and energy exchanges with the atmosphere. Good estimates of SSI at regional-to-global scales are needed for modeling land surface processes, climate and weather predictions, or management of solar power plants. This article presents the adaptation of the Mesoscale Atmospheric Global Irradiance Code (MAGIC)-Heliosat method used by the Climate Monitoring Satellite Application Facility (CM-SAF) for Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) to the Multifunction Transport Satellite 2 (MTSAT-2)/Imager and Himawari-8/Advanced Himawari Imager (AHI) sensors managed by the Japanese Meteorological Agency. The method allows providing estimates of global horizontal irradiance (GHI) and direct normal irradiance (DNI) over the Asian Pacific coast and Oceania. These estimates were evaluated by comparison to ground data measured at six baseline surface radiation network (BSRN) stations during years 2014 and 2016. The results showed that GHI can be determined with an accuracy of $-5,,text{W}cdot text{m}^{-2}$ , a precision of 160 $text{W}cdot text{m}^{-2}$ , and a relative absolute error of 30% in an hourly basis. They improved to an accuracy of $-5,,text{W}cdot text{m}^{-2}$ ( $-5,,text{W}cdot text{m}^{-2}$ ), a precision of 70 $text{W}cdot text{m}^{-2}$ (40 $text{W}cdot text{m}^{-2}$ ), and a relative error of 10% (7%) in-daily (monthly) estimates. The results for DNI showed an accuracy of $-45,,text{W}cdot text{m}^{-2}$ and a precision of 330 $text{W}cdot text{m}^{-2}$ , which represent a relative absolute error of 38%. These results improved for longer time steps, with an accuracy of +15 $text{W}cdot text{m}^{-2}$ (+30 $text{W}cdot text{m}^{-2}$ ), a precision of 150 $text{W}cdot text{m}^{-2}$ (130 $text{W}cdot text{m}^{-2}$ ), and a relative error of 35% (20%) in daily (monthly) estimations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Self-Supervised Global–Local Contrastive Learning for Fine-Grained
           Change Detection in VHR Images

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      Authors: Fenlong Jiang;Maoguo Gong;Hanhong Zheng;Tongfei Liu;Mingyang Zhang;Jialu Liu;
      Pages: 1 - 13
      Abstract: Self-supervised contrastive learning (CL) can learn high-quality feature representations that are beneficial to downstream tasks without labeled data. However, most CL methods are for image-level tasks. For the fine-grained change detection (FCD) tasks, such as change or change trend detection of some specific ground objects, it is usually necessary to perform pixel-level discriminative analysis. Therefore, feature representations learned by image-level CL may have limited effects on FCD. To address this problem, we propose a self-supervised global–local contrastive learning (GLCL) framework, which extends the instance discrimination task to the pixel level. GLCL follows the current mainstream CL paradigm and consists of four parts, including data augmentation to generate different views of the input, an encoder network for feature extraction, a global CL head, and a local CL head to perform image-level and pixel-level instance discrimination tasks, respectively. Through GLCL, features belonging to different perspectives of the same instance will be pulled closer, while features of different instances will be alienated, which can enhance the discriminativeness of feature representations from both global and local perspectives, thereby facilitating downstream FCD tasks. In addition, GLCL makes a targeted structural adaptation to FCD, i.e., the encoder network is undertaken by the common backbone networks of FCD, which can accelerate the deployment on downstream FCD tasks. Experimental results on several real datasets show that compared with other parameter initialization methods, the FCD models pretrained by GLCL can obtain better detection performance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Curvelet Adversarial Augmented Neural Network for SAR Image Classification

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      Authors: Yake Zhang;Fang Liu;Licheng Jiao;Shuyuan Yang;Lingling Li;Meijuan Yang;Jianlong Wang;Xu Liu;
      Pages: 1 - 17
      Abstract: Convolutional neural networks (CNNs) have superior feature learning capabilities with large numbers of labeled samples. The reality is that labeling these samples is costly in terms of human labor. Existing data augmentation methods alleviate the scarcity of labeled samples. However, these methods are not suitable for synthetic aperture radar (SAR) images, owing to special imaging mechanisms and observational objects. The generative SAR images by existing augmented methods show structure distortion. To address this issue, we introduce a curvelet adversarial augmented neural network (CA2NN) for SAR image classification. Specifically, an $text{A}^{2}$ NN is established, which consists of two generative streams and one discriminative stream. In the generative stream, through the mutual transformation between the whole and partial images, more new samples with structural consistency are generated to augment the limited labeled data. In the discriminative stream, these generated samples show certain appearance variations after adversarial training based on the novel joint discriminant criterion. Simultaneously, given the multiscale and multidirectional nature of SAR images, we construct discretized curvelet in 2-D space, aiming to extract the singularity features and avoid overfitting. By integrating curvelet kernels into $text{A}^{2}$ NN, CA2NN can automatically generate more representative features adapting to complex terrain, while greatly reducing the complexity of the network. Experiments are conducted on the SAR images with large-scale and complex scenes, suggesting that the proposed approach significantly improves the classification performance with few labeled samples.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Spatial Hierarchical Reasoning Network for Remote Sensing Visual
           Question Answering

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      Authors: Zixiao Zhang;Licheng Jiao;Lingling Li;Xu Liu;Puhua Chen;Fang Liu;Yuxuan Li;Zhicheng Guo;
      Pages: 1 - 15
      Abstract: For visual question answering on remote sensing (RSVQA), current methods scarcely consider geospatial objects typically with large-scale differences and positional sensitive properties. Besides, modeling and reasoning the relationships between entities have rarely been explored, which leads to one-sided and inaccurate answer predictions. In this article, a novel method called spatial hierarchical reasoning network (SHRNet) is proposed, which endows a remote sensing (RS) visual question answering (VQA) system with enhanced visual–spatial reasoning capability. Specifically, a hash-based spatial multiscale visual representation module is first designed to encode multiscale visual features embedded with spatial positional information. Then, spatial hierarchical reasoning is conducted to learn the high-order inner group object relations across multiple scales under the guidance of linguistic cues. Finally, a visual-question (VQ) interaction module is employed to learn an effective image–text joint embedding for the final answer predicting. Experimental results on three public RS VQA datasets confirm the effectiveness and superiority of our model SHRNet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Domain Adaptation Method for Land Use Classification Based on Improved
           HR-Net

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      Authors: Zezhong Zheng;Shuang Yu;Shaobin Jiang;
      Pages: 1 - 11
      Abstract: In recent years, the recognition accuracy of a semantic segmentation model on natural images can yield a very high level. Thus, it is of great significance to utilize semantic segmentation algorithm to obtain land use classification with remote sensing images. However, due to the large differences between natural images and remote sensing images, the standard semantic segmentation algorithm is not effective for land use classification of remote sensing images. In this article, the structure of high-resolution network (HR-Net) algorithm is improved according to the difference between the two kinds of images to make it more suitable for remote sensing images. Furthermore, in order to overcome the dependence of the semantic segmentation algorithm on a large number of high-quality prior data sets, some research experiments are conducted with the improved HR-Net domain adaptation model, and both of the adversarial domain adaptation model and the fusion domain adaptation model based on improved HR-Net and CycleGAN are designed to reduce the workload of manually labeling data. The extensive experimental results show that the classification of our improved HR-Net algorithm and the two domain adaptation models outperform other algorithms that demonstrates the effectiveness and superiority of our algorithms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Change Detection on Remote Sensing Images Using Dual-Branch Multilevel
           Intertemporal Network

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      Authors: Yuchao Feng;Jiawei Jiang;Honghui Xu;Jianwei Zheng;
      Pages: 1 - 15
      Abstract: Change detection (CD) of remote sensing (RS) images is mushrooming up accompanied by the on-going innovation of convolutional neural networks (CNNs). Yet with the high-speed technology upgrade, the obstacle that identifies unbalanced variations in foreground–background categories still lies on the table, especially in cases with limited samples and massive interference such as seasonal turnover, illumination intensity, and building reformation. Moreover, to date, neither of the off-the-shelf methods probes the feasibility of direct interaction between bitemporal images before accessing difference features. In this article, we propose a dual-branch multilevel intertemporal network (DMINet) to efficiently and effectively derive the change representations. Specifically, by unifying self-attention (SelfAtt) and cross-attention (CrossAtt) in a single module, we present an intertemporal joint-attention (JointAtt) block to steer the global feature distribution of each input, motivating information coupling between intralevel representations and meanwhile suppressing the task-irrelevant interferences. In addition, centering more on the detection of difference features, a reliable architecture is designed by spotlighting two concerns, i.e., the difference acquisition using subtraction and concatenation as well as the multilevel difference aggregation using incremental feature alignment. Based on a naive backbone without sophisticated structures, i.e., ResNet18, our model outperforms other state-of-the-art (SOTA) methods on four CD datasets, especially in cases with rarely samples. Moreover, the achievement is attained with light overheads.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • When Multigranularity Meets Spatial–Spectral Attention: A Hybrid
           Transformer for Hyperspectral Image Classification

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      Authors: Er Ouyang;Bin Li;Wenjing Hu;Guoyun Zhang;Lin Zhao;Jianhui Wu;
      Pages: 1 - 18
      Abstract: The transformer framework has shown great potential in the field of hyperspectral image (HSI) classification due to its superior global modeling capabilities compared to convolutional neural networks (CNNs). To utilize the transformer to model spatial–spectral information, a hybrid transformer that integrates multigranularity tokens and spatial–spectral attention (SSA) is proposed. Specifically, a token generator is designed to embed the multigranularity semantic tokens, which contributes richer image features to the model by exploiting CNN’s local representation capability. Moreover, a transformer encoder with an SSA mechanism is proposed to capture the global dependencies between different tokens, enabling the model to focus on more differentiated channels and spatial locations to improve the classification accuracy. Ultimately, adaptive weighted fusion is applied to different granularity transformer branches to boost HybridFormer’s classification performance. Experiments were conducted on four new challenging datasets, and the results indicate that HybridFormer achieves state-of-the-art results in terms of classification performance. The code of this work will be available at https://github.com/zhaolin6/HybridFormer for the sake of reproducibility.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep Feature Enhancement Method for Land Cover With Irregular and Sparse
           Spatial Distribution Features: A Case Study on Open-Pit Mining

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      Authors: Gaodian Zhou;Jiahui Xu;Weitao Chen;Xianju Li;Jun Li;Lizhe Wang;
      Pages: 1 - 20
      Abstract: Land cover classification in mining areas (LCMA) is essential for the environmental assessment of mines and plays a crucial role in their sustainable development. The shapes of mine land occupation elements are irregular, and the overall proportion of their area is relatively small. Therefore, their features may be easily lost during feature extraction, which limits the interpretation accuracy in mining areas. This study attempts to address these issues. We propose a model named EG-UNet to enhance the features of elements with few samples and to capture long-range information. The proposed EG-UNet includes two main modules. First, the edge feature enhancement module, the edges of elements of mine land occupation contain more information than other spatial locations. Hence, during the feature extraction of elements, a Sobel operator is used to extract the object boundary, which increases the weight of these features before the pooling operation for their preservation. Second, the long-range information extraction module, long-range information helps extract tiny objects, such as dumping grounds in the mining area. We present a graph convolutional network (GCN) to capture the long-range features and apply convolutional neural networks to learn the graph construction. A total of ten deep-learning networks were compared using the LCMA semantic segmentation dataset. Our model exhibited the best performance, especially in classifying classes with few samples. Furthermore, to evaluate the general ability of EG-UNet, a benchmark-Gaofen Image Dataset (GID) was used, and the result still reflected the superiority of our method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multisource Maximum Predictor Discrepancy for Unsupervised Domain
           Adaptation on Corn Yield Prediction

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      Authors: Yuchi Ma;Zhengwei Yang;Zhou Zhang;
      Pages: 1 - 15
      Abstract: Recently, with the advent of satellite missions and artificial intelligence techniques, supervised machine learning (ML) methods have been more and more used for analyzing remote sensing (RS) observation data for crop yield prediction. However, due to the domain shift between heterogeneous regions, supervised ML models tend to have poor spatial transferability. As a result, models trained with labeled data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a multisource maximum predictor discrepancy (MMPD) neural network that is an unsupervised domain adaptation (UDA) approach for corn yield prediction at the county level. The novelties of this study include that: 1) we proposed to maximize the discrepancy between two source-specific yield predictors and align source and target domains by considering crop yield response in the target domain and 2) we adopted the strategy of multisource UDA to avoid negative interference between labeled samples from different sources. Case studies in the U.S. corn belt and Argentina demonstrated that the proposed MMPD model had effectively reduced domain shifts and outperformed several other state-of-the-art deep learning (DL) and UDA methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Two-Stream Translating LSTM Network for Mangroves Mapping Using Sentinel-2
           Multivariate Time Series

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      Authors: Zhaohui Xue;Siyu Qian;
      Pages: 1 - 16
      Abstract: Monitoring mangroves is critical to protect the coastal ecosystems, and deep learning has gained great popularity in mapping mangroves using remote sensing. However, mangroves are usually submerged by cyclical tide since they are grown in land–sea interface places, resulting in some drawbacks of existing mangroves mapping models. On one side, the correlations between the vegetation index (VI) and the water index (WI) time series of mangroves are not fully considered. On another side, existing models rarely explored the local differences between mangroves and other land covers. Considering the above two aspects, we propose a novel two-stream translating long short-term memory network (TSTLN) for mangroves mapping. First, we construct multivariate time series (MTS) by compositing VI and WI based on Sentinel-2 time-series data. Second, we build a two-stream architecture and design a Siamese translating (ST) module in both streams. In the global stream, MTS is embedded into the ST module directly to get global features, whereas, in the local stream, a depthwise convolutional self-attention (DCA) module is conceived to capture local information first, and then, local features are further learned by the ST module. Finally, a fully connected layer and softmax are used to classify the representations extracted from the two streams. Experiments conducted over the Maowei Sea, the Dongzhai Port, and the Quanzhou Bay in 2019 demonstrate that: 1) TSTLN outperforms other methods, with improved OA of 0.49%–3.89%, 1.35%–6.85%, and 0.73%–3.65% in the three areas, respectively; 2) two-stream architecture, ST module, and DCA module all contribute to the good performance of TSTLN; and 3) TSTLN maintains higher accuracy with few parameters and less running time compared to ot-er counterparts.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Normalized Spectral Angle Index for Estimating the Probability of
           Viewing Sunlit Leaves From Satellite Data

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      Authors: Meihong Fang;Weimin Ju;Jing M. Chen;Weiliang Fan;Wei He;Feng Qiu;Xiangyan Hu;Jing Li;
      Pages: 1 - 19
      Abstract: The probability of viewing sunlit leaves (PT) is a crucial variable influencing observed canopy spectra. Proper determination of PT is necessary for the quantitative retrieval of vegetation parameters using remote sensing. This article describes a spectral index for estimating PT from satellite-observed canopy spectra. For this purpose, we propose a normalized spectral angle index (NSAI) at near-infrared (NIR) wavelengths, based on the spectral shapes of leaf and soil background. The performance of NSAI in estimating PT was evaluated using one ground-based high-resolution imaging dataset, one synthetic satellite dataset, and one satellite-ground synchronous observation dataset. The results demonstrate that NSAI is more suitable for estimating PT from satellite data than five commonly used spectral indices, including enhanced vegetation index (EVI), normalized difference spectral index (NDSI), normalized difference vegetation index (NDVI), simple ratio (SR) index, and photochemical reflectance index (PRI). NSAI exhibits a significant linear correlation with PT. The empirical model for estimating PT based on NSAI has the best transferability from simulated to in situ satellite data. For the fine spectral–spatial resolution (Hyperion) data, the normalized root-mean-square error (nRMSE) and adjusted $R^{2}$ of estimated PT were 14.9% and 0.744, respectively. For MODIS images, PT was estimated with satisfactory accuracy, with an nRMSE of 18.71% and an adjusted $R^{2}$ of 0.670. NSAI is potentially applicable to satellite images for direct estimation of PT to improve the inversion accuracy of vegetation parameters.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Exploring the Potential of Gaofen-1/6 for Crop Monitoring: Generating
           Daily Decametric-Resolution Leaf Area Index Time Series

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      Authors: Baodong Xu;Haodong Wei;Zhiwen Cai;Jingya Yang;Zhewei Zhang;Cong Wang;Jing Li;Jing Zhao;Yonghua Qu;Gaofei Yin;Aleixandre Verger;
      Pages: 1 - 14
      Abstract: High spatiotemporal resolution time series of leaf area index (LAI) are essential for monitoring crop dynamics and validating coarse-resolution LAI products. The optical satellite sensors at decametric resolution have historically suffered from a long revisit cycle and cloud contamination issues that hampered the acquisition of frequent and high-quality observations. The 16-m/four-day resolution of the new-generation Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites provide an unprecedented opportunity to address these limitations. Here, we developed an effective strategy to generate daily 16-m LAI maps combining GF-1/6 data and ground LAINet measurements. All high-quality GF-1/6 observations were utilized first to derive smoothed time series of vegetation indices (VIs). Second, a random forest regression (RF-r) model was trained to link the VIs with corresponding field LAI measurements. The trained RF-r was finally employed to generate the LAI maps. Results demonstrated the reliability of the reconstructed daily VIs (relative error (RE) < 1%) and the derived LAI time series, which greatly benefited from GF-1/6 high-frequency observations. The direct comparison with field LAI measurements by LAI-2200/LI-3000 showed the good performance of retrieved LAI maps, with bias, root mean square error (RMSE), and $R^{mathbf {2}}$ of 0.05, 0.59, and 0.75, respectively. The LAI time series well captured the spatiotemporal variation of crop growth. Furthermore, the continuous GF-1/6 LAI maps outperformed Sentinel-2 LAI estimates both in terms of temporal frequency and accuracy. Our study indicates the potential of GF-1/6 to generate continuous decametric-resolution LAI maps for fine-scale agricultural monitoring.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Pixel–Scene–Pixel–Object Sample Transferring: A Labor-Free Approach
           for High-Resolution Plastic Greenhouse Mapping

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      Authors: Peng Zhang;Shanchuan Guo;Wei Zhang;Cong Lin;Zilong Xia;Xingang Zhang;Hong Fang;Peijun Du;
      Pages: 1 - 17
      Abstract: As an important agriculture technique, plastic greenhouse (PG) has been widely used to increase crop yield and improve food security status in the world. The high-resolution spatial information of PG is of great significance to precise agricultural management and quantitative environmental assessment. Many studies have examined the role that remote sensing (RS) technology could play in mapping and monitoring PG coverage. However, these methods, which employ either the traditional machine learning algorithms or the deep learning models, depend on massive manually labeled samples. To address this problem, this article proposes a new cross-scale sample transferring method to generate high-resolution samples for automated PG mapping. The proposed method aims to transfer reliable label information from Sentinel-2 images (10 m) to high-resolution images (0.2 m) in a pixel–scene–pixel–object (PSPO) transferring process. In the proposed PG mapping workflow, the low-resolution label information of PG/non-PG can be obtained from an advanced plastic greenhouse index (APGI) which is calculated in Sentinel-2 images, and then, the label information is transferred to the corresponding high-resolution images using the proposed PSPO transferring method. Finally, the transferred high-resolution samples are used to train the deep semantic segmentation model and produce PG mapping results. The whole process is labor-free which requires no manually labeled samples. The experimental results on three collected datasets show that the proposed approach can automatically generate accurate and reliable high-resolution samples, and the final PG mapping results can achieve an overall accuracy (OA) of 89.52%–97.65% and F1 score of 84.13%–94.03%, which is comparable to the fully supervised semantic segmentation model.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • The Improved Winter Wheat Yield Estimation by Assimilating GLASS LAI Into
           a Crop Growth Model With the Proposed Bayesian Posterior-Based Ensemble
           Kalman Filter

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      Authors: Hai Huang;Jianxi Huang;Yantong Wu;Wen Zhuo;Jianjian Song;Xuecao Li;Li Li;Wei Su;Han Ma;Shunlin Liang;
      Pages: 1 - 18
      Abstract: Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. First, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m leaf area index (LAI) product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Second, the model posterior uncertainty was used in the ensemble Kalman filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated that the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination $R^{2}$ value increasing from 0.06 to 0.41, the mean absolute percentage error (MAPE) value decreasing from 12.65% to 7.82%, and the root-mean-square error (RMSE) value decreasing from 987 to 688 kg $cdot $ ha $^{-1}$ ) and the regional scale ( $R^{2}$ value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg $cdot $ ha $^{-1}$ ) compared with the open-loop estimation. Our analysis also indicated -hat the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Method to Estimate the Leaf Chlorophyll Content From Multiangular
           Measurements: Anisotropy Index

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      Authors: Zhongqiu Sun;Shan Lu;Kenji Omasa;
      Pages: 1 - 14
      Abstract: Anisotropy index (ANIX), which is defined as the ratio between the maximum and minimum reflectance factors in the principal plane, has been applied in characterizing the optical properties of vegetation, but it is seldom used to estimate the chlorophyll content in leaf level. In this study, we found the leaf spectral ANIX depended on the variation of leaf chlorophyll content (LCC). Newly proposed indices, [ASRI: $A_{750}/A_{720}$ and anisotropy normalized difference index (ANDI): ( $A_{750} - A_{720}$ )/( $A_{750}$ + $A_{720}$ ), A is the modified ANIX (mANIX = ANIX −1)], were derived from the format of the existing reflectance-based vegetation indices: simple ratio (SR) and ND indices. The diffuse reflection, closely related to LCC, can be extracted from the two new indices [or directly obtained as the minimum bidirectional reflectance factor (BRF)]. They have strong linear relationships with LCC ( $R^{2}=0.88$ and 0.89) and have good estimation accuracy using an independent dataset (RMSE = 6.39 and $6.10 mu text{g}$ /cm2). We also found that the ASRI (ANDI) had similar LCC estimation accuracy as the range-ASRIs or range-ANDIs (RANDIs or RANDIs), which were calculated by the random combination of reflectance factors in the forward scattering directions (ranged from −30° to −50°) and near the backward scattering directions (ranged from 0° to 20°). The w-de and effective ranges of viewing angles strengthen the usability of ASRI and ANDI to estimate LCC in the practical measurements.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • BCTNet: Bi-Branch Cross-Fusion Transformer for Building Footprint
           Extraction

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      Authors: Lele Xu;Ye Li;Jinzhong Xu;Yue Zhang;Lili Guo;
      Pages: 1 - 14
      Abstract: Building footprint extraction in remote sensing remains challenging due to the diverse appearances of buildings and confusing scenarios. Recently, researchers have revealed that both the globality and locality are vitally important in building footprint extraction tasks and proposed to incorporate the local context and global long-range dependency in the segmentation models. However, inadequate integration of the globality and locality still leads to incomplete, fake, or missing extraction results. To alleviate these problems, a novel segmentation method named bi-branch cross-fusion transformer network (BCTNet) is proposed in this study. Two parallel branches of the convolutional encoder branch (CB) and the transformer encoder branch (TB) are designed to extract multiscale feature maps. A concatenation-then-cross-fusion transformer block (CCTB) is put forward to integrate the locality from the CB and globality from the TB in a cross-fusion way at each stage of the encoding process. Then, an adaptive gating module (AGM) is proposed to gate the feature maps from the CCTB to strengthen the important features while suppressing irrelevant interference information. After that, the segmentation results can be obtained through a simple decoding process. Comprehensive experiments on two benchmark datasets demonstrate that the proposed BCTNet can achieve superior performance compared with the current state-of-the-art (SOTA) segmentation methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Ultralightweight Spatial–Spectral Feature Cooperation Network for Change
           Detection in Remote Sensing Images

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      Authors: Tao Lei;Xinzhe Geng;Hailong Ning;Zhiyong Lv;Maoguo Gong;Yaochu Jin;Asoke K. Nandi;
      Pages: 1 - 14
      Abstract: Deep convolutional neural networks (CNNs) have achieved much success in remote sensing image change detection (CD) but still suffer from two main problems. First, the existing multiscale feature fusion methods often use redundant feature extraction and fusion strategies, which often lead to high computational costs and memory usage. Second, the regular attention mechanism in CD is difficult to model spatial–spectral features and generate 3-D attention weights at the same time, ignoring the cooperation between spatial features and spectral features. To address the above issues, an efficient ultralightweight spatial–spectral feature cooperation network (USSFC-Net) is proposed for CD in this article. The proposed USSFC-Net has two main advantages. First, a multiscale decoupled convolution (MSDConv) is designed, which is clearly different from the popular atrous spatial pyramid pooling (ASPP) module and its variants since it can flexibly capture the multiscale features of changed objects using cyclic multiscale convolution. Meanwhile, the design of MSDConv can greatly reduce the number of parameters and computational redundancy. Second, an efficient spatial–spectral feature cooperation (SSFC) strategy is introduced to obtain richer features. The SSFC differs from the existing 2-D attention mechanisms since it learns 3-D spatial–spectral attention weights without adding any parameters. The experiments on three datasets for remote sensing image CD demonstrate that the proposed USSFC-Net achieves better CD accuracy than most CNNs-based methods and requires lower computational costs and fewer parameters, even it is superior to some Transformer-based methods. The code is available at https://github.com/SUST-reynole/USSFC-Net.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • From Trained to Untrained: A Novel Change Detection Framework Using
           Randomly Initialized Models With Spatial–Channel Augmentation for
           Hyperspectral Images

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      Authors: Bin Yang;Yin Mao;Licheng Liu;Xinxin Liu;Yuzhong Ma;Jing Li;
      Pages: 1 - 14
      Abstract: Deep learning (DL) approaches have been extensively applied to change detection in hyperspectral images (HSIs). However, the majority of them encounter scarcity of training samples or rely on complex structures and learning strategies. Although untrained change detection models have been proved to be effective in relieving the above problems, they were constructed using regular convolutions and treated spatial locations and channels equally, which are insufficient to extract discriminative features and lead to limited accuracy. Given this, a novel untrained framework using randomly initialized models with spatial–channel augmentation (RICD) is proposed for HSI change detection in this article. It consists of two major modules: 1) an enhanced feature extraction network using successive dilation-deformable feature extraction blocks, which can extract multiscale spatial–spectral features over unfixed sampling locations. It enlarges the field of view of convolutions and takes arbitrary neighborhood into consideration, which helps to increase the discriminativeness of the extracted features. And 2) a change-sensitive feature augmentation and comparison module integrating feature selection and spatial–channel augmentation strategies, which can exploit spatial context and channel importance. It magnifies difference between changed pixels and unchanged ones and emphasizes contribution of significant channels of the selected change-sensitive features. Despite that convolution operations are included in RICD, all the weights are untrained and fixed once they are randomly initialized, indicating that the RICD can work in an unsupervised manner. Its performance is tested over three widely used hyperspectral datasets. Quantitative and qualitative comparisons with several state-of-the-art unsupervised methods reveal the effectiveness of the RICD method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Improving the Quality of MODIS LAI Products by Exploiting Spatiotemporal
           Correlation Information

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      Authors: Jingrui Wang;Kai Yan;Si Gao;Jiabin Pu;Jinxiu Liu;Taejin Park;Jian Bi;Eduardo Eiji Maeda;Janne Heiskanen;Yuri Knyazikhin;Ranga B. Myneni;
      Pages: 1 - 19
      Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product is critical for global terrestrial carbon monitoring and ecosystem modeling. However, MODIS LAI is calculated on a pixel-by-pixel and day-by-day basis without using spatial or temporal correlation information, which leads to its high sensitivity of LAI to uncertainties in observed reflectance, resulting in an increased noise level in time series. While exploiting prior knowledge is a common practice to fill gaps in observations, little research has been conducted on reducing noisy fluctuations and improving the overall quality of the MODIS LAI product. To address this issue, we proposed a spatiotemporal information composition algorithm (STICA), which directly introduces prior spatiotemporal correlation and multiple quality assessment (MQA) information into the existing MODIS LAI product. STICA reduces the noise level and improves the quality of the product while maintaining the original physically based (radiative transfer model, RTM) LAI production process. In our analysis, the $R^{2}$ increased from 0.79 to 0.81 and the root-mean-square error (RMSE) decreased from 0.81 to 0.68 compared to the ground-based LAI reference. The improvement was more pronounced with the degradation of the data quality. STICA reduced noisy fluctuations in the LAI time series to varying degrees among eight biome types. In the Amazon forest, STICA significantly improved the time-series stability of LAI. Moreover, STICA can effectively eliminate abnormal declines in time series and correct for extreme outliers in LAI. We expect that the MODIS LAI reanalyzed product generated by this method will better support the application of high-quality LAI datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Enhancement of Nighttime Fire Detection and Combustion Efficiency
           Characterization Using Suomi-NPP and NOAA-20 VIIRS Instruments

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      Authors: Meng Zhou;Jun Wang;Lorena Castro Garcia;Xi Chen;Arlindo M. da Silva;Zhuosen Wang;Miguel O. Román;Edward J. Hyer;Steven D. Miller;
      Pages: 1 - 20
      Abstract: We present the second-generation FIre Light Detection Algorithm (FILDA-2), which includes advances in fire detection and retrievals of radiative power (FRP), fire visible energy fraction (VEF), and fire modified combustion efficiency (MCE) at nighttime from the holistic use of multiple-spectral radiances measured by the visible infrared imaging radiometer suite (VIIRS) aboard Suomi-NPP (VNP) and National Oceanic and Atmospheric Administration (NOAA)-20/joint polar satellite system (JPSS)-1 (VJ1) satellites. Key enhancements include: 1) a new fast algorithm that maps VIIRS day/night band (DNB) radiances to the pixel footprints of VIIRS moderate (M) and imagery (I) bands; 2) identification of potential fire pixels through the use of the DNB anomalies and I-band thermal anomalies; 3) dynamic thresholds for contextual testing of fire pixels; and 4) pixel-specific estimates of FRP, VEF, and MCE. The global benchmark test demonstrates that FILDA-2 can detect approximately 25%–30% smaller and cooler fires than the operational VIIRS active fire 375-m I-band algorithm with the added benefit of providing daily global pixel-level characterizations of MCE for nighttime surface fires. The MCE derived by FILDA-2 is in good agreement with limited ground-based observations near the fires. Additionally, FILDA-2 reduces angular dependence in FRP estimates and significantly reduces the “bow-tie” (double-counting) effect in fire detection compared with the AF-I product. The cross-validation of FILDA-2 products from VNP and VJ1 retrievals confirms good consistency in FRP and MCE retrievals globally. FILDA-2 is being implemented by the National Aeronautics and Space Administration (NASA) to generate a new VIIRS data product for fire monitoring, chemical-speciated fire emission estimates, and fire line characterization.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Impact of Vegetation Gradient and Land Cover Conditions on Soil Moisture
           Retrievals From Different Frequencies and Acquisition Times of AMSR2

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      Authors: Muhammad Zohaib;Hyunglok Kim;Venkataraman Lakshmi;
      Pages: 1 - 14
      Abstract: Spaceborne remote sensing provides great potential for soil moisture (SM) retrieval and emerged as a significant data source for research in land surface dynamics and associated applications. This study compared the error characteristics of SM estimates retrieved from the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board National Aeronautics and Space Administration (NASA)’s Aqua satellite across different vegetation gradients and land cover conditions, at different overpass times and frequencies, to demonstrate their strengths and limitations. The results demonstrate that AMSR2 C-band products outperform AMSR2 X-band products over moderately and densely vegetated conditions due to lower attenuation by the vegetation canopy. Conversely, X-band products performed better than C-band products in barren lands possibly due to uneven sensing depth and microwave emissions from subsurface in C-band. The daytime products have a higher signal-to-noise ratio (SNR) in sparsely and moderately vegetated areas, whereas nighttime products have a higher SNR in densely vegetated areas. When these products are used selectively based on their error characteristics, the probability of obtaining SM with stronger signal than noise can be significantly improved (95%) at the expense of impaired spatial coverage (70% pixel loss).
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • MSH-Net: Modality-Shared Hallucination With Joint Adaptation Distillation
           for Remote Sensing Image Classification Using Missing Modalities

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      Authors: Shicai Wei;Yang Luo;Xiaoguang Ma;Peng Ren;Chunbo Luo;
      Pages: 1 - 15
      Abstract: Learning-based multimodal data has attracted increasing interest in the remote sensing community owing to its robust performance. Although it is preferable to collect multiple modalities for training, not all of them are available in practical scenarios due to the restriction of imaging conditions. Therefore, how to assist the model inference with missing modalities is significant for multimodal remote sensing image processing. In this work, we propose a general framework called modality-shared hallucination network (MSH-Net) to address this issue by reconstructing complete modality-shared features from incomplete inference modalities. Compared to conventional privilege modality hallucination methods, MSH-Net does not only help preserve the cross-modal interactions for model inference but also scales well with the increasing number of missing modalities. We further develop a novel joint adaptation distillation (JAD) method that guides the hallucination model to learn the modality-shared knowledge from the multimodal model by matching the joint probability distributions between representation and groundtruth. This overcomes the representation heterogeneity caused by the discrepancy between inputs and structures of multimodal and hallucination model while preserving the decision boundaries refined by multimodal cues. Finally, extensive experiments conducted on four common modality combinations demonstrate that the proposed MSH-Net can effectively address the problem of missing modalities and achieve state-of-the-art performance. Code is available at: https://github.com/shicaiwei123/MSHNet
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • TSCMDL: Multimodal Deep Learning Framework for Classifying Tree Species
           Using Fusion of 2-D and 3-D Features

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      Authors: Bingjie Liu;Yuanshuo Hao;Huaguo Huang;Shuxin Chen;Zengyuan Li;Erxue Chen;Xin Tian;Min Ren;
      Pages: 1 - 11
      Abstract: Accurate tree species information is a prerequisite for forest resource management. Combining light detection and ranging (LiDAR) and image data is one main method of tree species classification. Traditional machine learning methods rely on expert knowledge to calculate a large number of feature parameters. Deep learning technology can directly use the original image and point cloud data to classify tree species. However, data with different patterns require the use of different types of deep learning methods. In this study, a tree species classification multimodal deep learning (TSCMDL) that fuses 2-D and 3-D features was constructed and then used to combine data from multiple sources for tree species classification. This framework uses an improved version of the PointMLP model as its backbone network and uses ResNet50 and PointMLP networks to extract the image features and point cloud features, respectively. The proposed framework was tested using unmanned aerial vehicle LiDAR (UAV LiDAR) data and red, green, blue (RGB) orthophotos. The results showed that the accuracy of the tree species classification using the TSCMDL framework was 98.52%, which was 4.02% higher than that based on point cloud features only. In addition, when the same hyperparameters were used for training the model, the efficiency of the model training was not significantly lower than for models based on point cloud features only. The proposed multimodal deep learning framework extracts features directly from the original data and integrates them effectively, thus avoiding manual feature screening and achieving more accurate classification. The feature extraction network used in the TSCMDL framework can be replaced by other suitable frameworks and has strong application potential.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Mesh-Based DGCNN: Semantic Segmentation of Textured 3-D Urban Scenes

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      Authors: Rongting Zhang;Guangyun Zhang;Jihao Yin;Xiuping Jia;Ajmal Mian;
      Pages: 1 - 12
      Abstract: Textured 3-D mesh is one of the final user products in photogrammetry and remote sensing. However, research on the semantic segmentation of complex urban scenes represented by textured 3-D meshes is in its infancy. We present a mesh-based dynamic graph convolutional neural network (DGCNN) for the semantic segmentation of textured 3-D meshes. To represent each mesh facet, composite input feature vectors are constructed by concatenating the face-inherent features, i.e., $XYZ$ coordinates of the center of gravity (CoG), texture values, and normal vectors (NVs). A texture fusion module is embedded into the proposed mesh-based DGCNN to generate high-level semantic features of the high-resolution texture information, which is useful for semantic segmentation. We achieve competitive accuracies when the proposed method is applied to the SUM mesh datasets. The overall accuracy (OA), Kappa coefficient (Kap), mean precision (mP), mean recall (mR), mean F1 score (mF1), and mean intersection over union (mIoU) are 93.3%, 88.7%, 79.6%, 83.0%, 80.7%, and 69.6%, respectively. In particular, the OA, mean class accuracy (mAcc), mIoU, and mF1 increase by 0.3%, 12.4%, 3.4%, and 6.9%, respectively, compared with the state-of-the-art method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Semantic Labeling of High-Resolution Images Using EfficientUNets and
           Transformers

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      Authors: Hasan Almarzouqi;Lyes Saad Saoud;
      Pages: 1 - 13
      Abstract: Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous quantities of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, due to the large size and high spatial resolution of remote sensing images, these networks cannot efficiently analyze an entire scene. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this article, we propose a new segmentation model that combines CNNs with transformers and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers that are designed to efficiently represent multimodal inputs and outputs of the network. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps [digital surface model (DSM)]. The output fusion layer uses a novel multitask segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method (FMM) is used to convert unidentified class labels into their closest known neighbors. Our results demonstrate that the proposed method improves segmentation accuracy compared with state-of-the-art techniques.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Evaluation of Reflectance and Canopy Scattering Coefficient Based
           Vegetation Indices to Reduce the Impacts of Canopy Structure and Soil in
           Estimating Leaf and Canopy Chlorophyll Contents

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      Authors: Yingying Li;Shunlin Liang;
      Pages: 1 - 15
      Abstract: Chlorophyll is of great physiological and ecological significance. Leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be retrieved from remotely sensed data based on vegetation indices (VIs). However, the impacts of canopy structure and soil remain open problems. VIs are typically calculated from spectral reflectance. In this study, we also constructed and examined VIs based on canopy scattering coefficients ( $W_{lambda }$ ) from spectral invariants theory. Based on extensive leaf and canopy radiative transfer simulations, linear regression and artificial neural network models were built with reflectance-based and $W_{lambda }$ -based VIs to retrieve LCC and CCC. The results showed that the canopy structure and soil significantly affected the retrievals. $W_{lambda }$ can effectively suppress the impacts of the leaf angle distribution (LAD) but not the leaf area index (LAI). The $W_{lambda }$ , estimated as the ratio reflectance/directional area scattering factor (DASF), contained a large error when the soil effect was strong. The $W_{lambda }$ -based VIs did not yield very accurate results in LCC estimation but exhibited higher accuracy for CCC estimation compared to reflectance-based VIs. Of all the VIs investigated, the best VI was D99 [( $R_{850}$ – $R_{710}$ )/( $R_{850}$ – $R_{680}$ )] for LCC and Wmul ( $W_{749} times W_{956}$ ) for CCC. Compared to D99 for LCC, Wmul for CCC was less accurate, and the accuracy varied more among canopies with different LADs. The main reason was that CCC equals LCC multiplied by LAI, but LCC and LAI impact VIs in a similar manner.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Photon Cloud Filtering Method in Forested Areas Considering the Density
           Difference Between Canopy Photons and Ground Photons

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      Authors: Yi Li;Haiqiang Fu;Jianjun Zhu;Li Wang;Rong Zhao;Changcheng Wang;
      Pages: 1 - 14
      Abstract: Photon cloud data filtering is crucial when obtaining forest vertical structure parameters from photon-counting LiDAR data. The proposed method, for the first time, takes into account the influence of the density difference between canopy photons and ground photons. A moving overlapping window approach is introduced to reduce the impact of an uneven background noise environment first. In each window, a modified elevation histogram statistics vector in the elevation direction is proposed to increase the density difference between signal and noise photons while also reducing the density difference between canopy and ground photons. The filtering results show that the average overall accuracy (OA) and standard deviation of the proposed method reach almost 0.99 and 0.01, respectively, which are much better results than those of the other existing filtering methods. Specifically, with the increase in the ratio of canopy photons to ground photons, the F-measure value of the proposed method reaches almost 0.99, and is also stable, which demonstrates that the proposed approach can almost completely eliminate the influence of the density difference between canopy photons and ground photons on the filtering results. In addition, the forest canopy heights obtained based on the proposed filtering method achieve the lowest root-mean-square error (RMSE) value of 3.18 m, compared to the other filtering methods. In summary, the proposed photon cloud data filtering method can retrieve reliable forest canopy height information from photon cloud data, and outperforms the other compared filtering methods in the given test site.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Hybrid Siamese Network With Spatiotemporal Enhancement and Two-Level
           Feature Fusion for Remote Sensing Image Change Detection

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      Authors: Liangliang Yan;Jie Jiang;
      Pages: 1 - 17
      Abstract: With the popularization and development of deep learning (DL) technology, remote sensing (RS) image change detection (CD) has achieved remarkable success. However, an accurate CD has still been challenging due to the difficulties in achieving efficient feature extraction and effective difference feature enhancement and refinement. To address these limitations, this article proposes a hybrid Siamese network with spatiotemporal enhancement and two-level feature fusion (named the HSSENet) for CD. First, an efficient hybrid Siamese backbone is designed by combining a transformer’s advantage to capture dense dependencies between features and convolutional neural network (CNN)’s advantage to provide local prior knowledge. In addition, to reduce irrelevant pseudo-changes and high-frequency noise while maintaining the high compactness of changed targets, a spatiotemporal enhancement module (STEM) that adopts the self-attention mechanism for context modeling in spatiotemporal dimensions and can separately process low and high frequencies is proposed for effective difference feature enhancement. Finally, three two-level feature fusion modules (TL-FFMs) are designed instead of standard decoders to aggregate low-level details and high-level semantics for refining the boundary information. The proposed HSSENet is verified by experiments, and the experimental results demonstrate that it can obtain a better tradeoff between accuracy and efficiency than the state-of-the-art methods and significantly outperforms them with the F1-score of 91.48/91.55/91.17 points on the learning, vision, and RS (LEVIR)/Wuhan University (WHU)/deeply supervised image fusion network (DSIFN) test sets, respectively.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Improved Estimation of Leaf Area Index by Reducing Leaf Chlorophyll
           Content and Saturation Effects Based on Red-Edge Bands

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      Authors: Zhewei Zhang;Wenjie Jin;Ruyu Dou;Zhiwen Cai;Haodong Wei;Tongzhou Wu;Sen Yang;Meilin Tan;Zhijuan Li;Cong Wang;Gaofei Yin;Baodong Xu;
      Pages: 1 - 14
      Abstract: Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. The empirical-based model using vegetation indices (VIs) is an effective method for LAI estimation at the regional scale. However, due to the complexity of canopy radiation interaction processes, the leaf chlorophyll content ( $C_{ab}$ ) and saturation effects on canopy reflectance restrict the accuracy of VI-based LAI retrieval. To address these limitations, we propose a novel chlorophyll-insensitive VI (CIVI) using red, red-edge, and near-infrared (NIR) bands to improve regional LAI mapping. The CIVI was developed based on the sensitivity analysis of red-edge band reflectance to LAI and $C_{ab}$ using the simulation dataset from the PROSAIL model. Then, the performance of CIVI was carefully evaluated from two aspects: the sensitivity of VI to LAI and other parameters and the accuracy of LAI estimates using different VIs over homogeneous (cropland and grassland) and nonhomogeneous (forest) biome canopies. The results suggested that CIVI can capture LAI variations well while remaining insensitive to $C_{ab}$ variations. Additionally, the sensitivity of CIVI to other vegetation biochemical and biophysical parameters did not increase significantly compared to that of other VIs. Furthermore, CIVI exhibited the best performance of LAI retrievals over both homogeneous ( $R^{2}=0.938$ , RMSE = 0.447, and rRMSE = 21.3%) and nonhomogenous ( $R^{2}=0.635$ , RMSE = 0.693, and rRMSE = 14.0%) canopies amon- all selected VIs, especially for the high LAI. Our results indicated that the developed CIVI incorporating red-edge bands with a suitable formula can effectively reduce the $C_{ab}$ and saturation effects, which is promising for improving VI-based LAI estimation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Real-Time Aerial Detection and Reasoning on Embedded-UAVs in Rural
           Environments

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      Authors: Tin Lai;
      Pages: 1 - 7
      Abstract: We present a unified pipeline architecture for a real-time detection system on an embedded system for unmanned aerial vehicles (UAVs). Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the tradeoff. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multiactivities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model’s accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open field.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Lightweight Attention Network for Very High-Resolution Image Semantic
           Segmentation

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      Authors: Renchu Guan;Mingming Wang;Lorenzo Bruzzone;Haishi Zhao;Chen Yang;
      Pages: 1 - 14
      Abstract: Semantic segmentation is one of the most challenging tasks for very high-resolution (VHR) remote sensing applications. Deep convolutional neural networks (DCNNs) based on the attention mechanism have shown outstanding performance in VHR remote sensing images semantic segmentation. However, the existing attention-guided methods require the estimation of a large number of parameters that are affected by the limited number of available labeled samples that results in underperforming segmentation results. In this article, we propose a multistage feature fusion lightweight (MSFFL) model to greatly reduce the number of parameters and improve the accuracy of semantic segmentation. In this model, two parallel enhanced attention modules, i.e., the spatial attention module (SAM) and the channel attention module (CAM), are designed by introducing encoding position information. Then, a covariance calculation strategy is adopted to recalibrate the generated attention maps. The integration of enhanced attention modules into the proposed lightweight module results in an efficient lightweight attention network (LiANet). The performance of the proposed LiANet is assessed on two benchmark datasets. Experimental results demonstrate that LiANet can achieve promising performance with a small number of parameters.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • DCI-PGCN: Dual-Channel Interaction Portable Graph Convolutional Network
           for Landslide Detection

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      Authors: Weiming Li;Yibin Fu;Shuaishuai Fan;Mingrui Xin;Hongyang Bai;
      Pages: 1 - 16
      Abstract: Landslide, a kind of destructive natural disaster, often occurs in the mountainous areas of China. Landslide information instant collection plays an important role in taking appropriate remedial measures and personnel evacuation. In recent years, the use of convolutional neural network (CNN) for landslide regional detection achieved good performance; however, most CNN-based methods had no regard for the internal connection of the cover materials in the disaster occurrence area. Moreover, the information revealed by the internal deformation features was ignored, and the same surface object in the image presents different features under different illumination, environment, and resolution, which makes it difficult to extract the structural features of landslide images. In this article, we propose a novel graph convolutional network for landslide detection, inspired by attention mechanism’s ability to focus on selective information supplemented with both different channels. The global maximum node connection strategy with positive and negative connectivity makes the graph convolution network (GCN) more portable, which is used as the basic unit of graph feature propagation to construct a multilayer residual connection module. In order to learn interactively and spread graph information, channel dimension is added to make the boundary of features between classes more discriminative. Extensive experiments on Sichuan Province and Bijie landslide datasets show that our proposed method outperforms other detection models and achieves high precision and accuracy. In addition, we also carried out landslide detection for Zhaotong of Yunnan Province on GF-2 original images to prove the effectiveness and applicability of the algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Mining Joint Intraimage and Interimage Context for Remote Sensing Change
           Detection

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      Authors: Feng Zhou;Chao Xu;Renlong Hang;Rui Zhang;Qingshan Liu;
      Pages: 1 - 12
      Abstract: Recent deep learning methods for change detection focus on excavating more discriminative context within individual images. However, due to seasonal change, noise, and so on, the appearance of objects tends to be more heterogeneous among various scenes. Consequently, the above intraimage context is inadequate to represent specific-category objects and pseudo changes would be inevitable in detection results. To deal with this issue, we propose a context aggregation network (CANet) to mine interimage context over all training images for further enhancing intraimage context. Specifically, a Siamese network attached with temporal attention modules is served as a feature encoder to extract multiscale temporal features from bitemporal images. Then, a context extraction module is devised to capture long-range spatial–channel context within individual images. Meanwhile, context representations of underlying categories in the scene are inferred using all training images in an unsupervised manner. Finally, these two kinds of contextual information are aggregated to one which is subsequently fed into a multiscale fusion module to produce the detection map. CANet is compared with several state-of-the-art methods on three benchmark datasets, including the season-varying change detection (SVCD) dataset, the Sun Yat-sen University change detection (SYSU-CD) dataset, and the Learning Vision and Remote Sensing Laboratory building change detection (LEVIR-CD) dataset. It is demonstrated that our method outperforms all comparison methods in terms of F1, overall accuracy (OA), and intersection of union (IoU). The results of CANet on three datasets are available at https://github.com/NuistZF/CANet-for-change-detection and codes will be made public soon.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Segmentation Is Not the End of Road Extraction: An All-Visible Denoising
           Autoencoder for Connected and Smooth Road Reconstruction

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      Authors: Lingyi Han;Lu Hou;Xiangxiang Zheng;Ziyue Ding;Haojun Yang;Kan Zheng;
      Pages: 1 - 18
      Abstract: With a plethora of remote sensing (RS) images, deep neural network-based semantic segmentation model (SegModel) achieves commendable road extraction performance. However, the occlusions caused by vehicles, roadside objects, and shadows cannot be directly identified as road pixels, especially on high-resolution RS images. Therefore, relying only on a single SegModel to guarantee road connectivity and boundary smoothness in road extraction tasks is extremely difficult. To address this issue, this article puts forward a “segmentation-with-reconstruction” framework, which comprises a SegModel to generate the binary road labels from RS images and a reconstruction model to refine the road labels. Specifically, the former can be compatible with arbitrary existing SegModels, while the latter is built by our proposed model named all-visible denoising autoencoder (AV-DAE). The AV-DAE is designed to be an encoder–decoder architecture that takes topology-corruption road labels as inputs and true road labels as outputs. To better train the AV-DAE, we further present three noise-adding strategies to corrupt road labels for diverse patterns and train the AV-DAE to reconstruct them. Being RS-image-agnostic, the AV-DAE pays more attention to the spatial features rather than the spectral features, which enables it to recover the road topology by improving the connectivity and boundary smoothness. Finally, elaborate simulation results demonstrate that the proposed framework can significantly improve the connectivity and boundary smoothness of the extracted roads while achieving a competitive road extraction performance and high generalization ability compared to the benchmarks.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Method for Hidden Natural Caves Characterization and Accessibility
           Assessment From Spaceborne VHR SAR Images

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      Authors: Leonardo Carrer;Davide Castelletti;Riccardo Pozzobon;Francesco Sauro;Lorenzo Bruzzone;
      Pages: 1 - 11
      Abstract: Caves are one of the last frontiers of human exploration on Earth. They are very relevant scientific targets as they host significant biodiversity and unique geologic formations. The presence of underground passages accessible for human or robotic exploration are revealed by localized collapse of the near-surface ceiling of a cave system (skylight). Remote sensing systems are a valuable tool for skylights detection as these features are often located on very remote and often inaccessible regions of the Earth. However, with the available remote sensing techniques and data analysis methodologies, it is very difficult to determine whether a skylight is providing access to a cave continuation or it represents only a closed depression with no extensions. In this article we propose a methodology, based on very high-resolution (VHR) orbital synthetic aperture radar (SAR) imaging systems, to estimate both caves geometric characteristics and accessibility information in the proximity of a skylight. To test our methodology, we acquired radar data over different Earth’s location by exploiting the Capella Space X-band microsatellite radar constellation. The experimental results show that our methodology effectively determines the caves geometric characteristics and accessibility under a variety of surface conditions. We also detected several unknown and unexplored large cave systems located near Volcan Wolf and Ecuador, Isla Isabela, Galapagos. The presented work has relevant implications for the field of geological studies, ecology, and space exploration research since optical imaging shows the evidence of potential cave systems accessible from skylights on other planetary bodies such as Mars.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Efficient EM Modeling Scheme for Large 3-D Models—A
           Magnetotelluric Case Study

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      Authors: Arun Singh;Rahul Dehiya;
      Pages: 1 - 11
      Abstract: We present an efficient scheme for computing 3-D magnetotelluric (MT) forward responses. The scheme is especially valuable for large models resulting from fine discretization or the large survey area. The proposed approach overcomes the iterative solvers’ slow convergence that occurs in large modeling problems due to a sizeable ill-conditioned system matrix that needs to be solved. Primarily, the slow convergence arises due to the grid stretching that is necessary to apply the boundary conditions (BCs). Our approach partly removes the grid stretching, thus improving the computational efficiency. In this scheme, a model is represented using two different meshes. One is a coarse mesh with grid stretching, and another is a fine mesh of the desired discretization excluding grid stretching. Using the electric field computed for the coarse mesh, a radiation boundary (RB) vector is calculated at the outer boundary of the fine mesh and is used to compute the necessary BCs along with an initial guess to be utilized by the iterative solver for the fine mesh. The RB vector can be computed at any arbitrarily shaped interface, thus allowing more flexibility in the shape of the fine mesh boundary. It is a significant advantage compared to the traditional finite difference (FD)-based algorithms where the boundaries must be same as the cuboid surfaces. Through different resistivity models, both synthetic and real, we demonstrate that the proposed approach improves the computational efficiency without compromising the accuracy of the solution while providing more flexibility in the shape of the fine mesh.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Feature-Fusion Segmentation Network for Landslide Detection Using
           High-Resolution Remote Sensing Images and Digital Elevation Model Data

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      Authors: Xinran Liu;Yuexing Peng;Zili Lu;Wei Li;Junchuan Yu;Daqing Ge;Wei Xiang;
      Pages: 1 - 14
      Abstract: Landslide is one of the most dangerous and frequently occurred natural disasters. The semantic segmentation technique is efficient for wide area landslide identification from high-resolution remote sensing images (HRSIs). However, considerable challenges exist because the effects of sediments, vegetation, and human activities over long periods of time make visually blurred old landslides very challenging to detect based upon HRSIs. Moreover, for terrain features like slopes, aspect and altitude variations cannot be sufficiently extracted from 2-D HRSIs but can be from digital elevation model (DEM) data. Then, a feature-fusion based semantic segmentation network (FFS-Net) is proposed, which can extract texture and shape features from 2-D HRSIs and terrain features from DEM data before fusing these two distinct types of features in a higher feature layer. To segment landslides from background, a multiscale channel attention module is purposely designed to balance the low-level fine information and high-level semantic features. In the decoder, transposed convolution layer replaces original mathematical bilinear interpolation to better restore image resolution via learnable convolutional kernels, and both dropout and batch normalization (BN) are introduced to prevent over-fitting and accelerate the network convergence. Experimental results are presented to validate that the proposed FFS-Net can greatly improve the segmentation accuracy of visually blurred old landslides. Compared to U-Net and DeepLabV3+, FFS-Net can improve the mean intersection over union (mIoU) metric from 0.508 and 0.624 to 0.67, the F1 metric from 0.254 and 0.516 to 0.596, and the pixel accuracy (PA) metric from 0.874 and 0.906 to 0.92, respectively. For the detection of visually distinct landslides, FFS-NET also offers comparable detection performance, and the segmentation is improved for visually distinct landslides with similar color and texture to surroundings.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiform Ensemble Self-Supervised Learning for Few-Shot Remote Sensing
           Scene Classification

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      Authors: Jianzhao Li;Maoguo Gong;Huilin Liu;Yourun Zhang;Mingyang Zhang;Yue Wu;
      Pages: 1 - 16
      Abstract: Self-supervised learning is an effective way to solve model collapse for few-shot remote sensing scene classification (FSRSSC). However, most self-supervised contrastive learning auxiliary tasks perform poorly on the high interclass similarity problem in FSRSSC. Furthermore, it is time-consuming and computationally expensive to obtain the best combination among numerous self-supervised auxiliary tasks. In practical applications, we may encounter difficulties in remote sensing data acquisition and labeling, while most FSRSSC studies only focus on the former. To alleviate the above problems, we propose a multiform ensemble self-supervised learning (MES2L) framework for FSRSSC in this article. Based on the transfer learning-based few-shot scheme, we design a novel global–local contrastive learning auxiliary task to solve the low interclass separability problem. The self-attention mechanism is designed in the local contrast features to investigate the intrinsic associations between different remote sensing scene objectives. We also present a multiform ensemble enhancement (MEE) training method. Ensemble enhancement involves the concatenation of features extracted from different backbones trained by a combination of multiform self-supervised auxiliary tasks. MEE can not only be regarded as a more straightforward alternative to knowledge distillation but also can achieve an effective compromise between expensive computational cost and classification accuracy. In addition, we provide two scene classification schemes of inductive and transductive settings, corresponding to solving the difficulties of remote sensing data acquisition and labeling. The proposed network achieves state-of-the-art results on three benchmark FSRSSC datasets. The potential of the MES2L framework is also demonstrated in combination with classical metalearning-based and metric learning-based few-shot algorithms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Seismic Inversion Based on Acoustic Wave Equations Using Physics-Informed
           Neural Network

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      Authors: Yijie Zhang;Xueyu Zhu;Jinghuai Gao;
      Pages: 1 - 11
      Abstract: Seismic inversion is a significant tool for exploring the structure and characteristics of the underground. However, the conventional inversion strategy strongly depends on the initial model. In this work, we employ the physics-informed neural network (PINN) to estimate the velocity and density fields based on acoustic wave equations. In contrast to the traditional purely data-driven machine learning approaches, PINNs leverage both available data and the physical laws that govern the observed data during the training stage. In this work, the first-order acoustic wave equations are embedded in the loss function as a regularization term for training the neural networks. In addition to the limited amount of measurements about the state variables available at the surface being used as the observational data, the well logging data is also used as the direct observational data about the model parameters. The numerical results from several benchmark problems demonstrate that given noise-free or noisy data, the proposed inversion strategy is not only capable of predicting the seismograms, but also estimating the velocity and density fields accurately. Finally, we remark that although the absorbing boundary conditions are not imposed in the proposed method, the reflected waves do not appear from the artificial boundary in the predicted seismograms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Seismic Data Enhancement Based on Common-Reflection-Surface-Based Local
           Slope and Trimmed Mean Filter

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      Authors: Chuangjian Li;Suping Peng;Xiaoqin Cui;Wenfeng Du;Peng Lin;
      Pages: 1 - 9
      Abstract: Seismic data often contain noise that can disturb or mask effective information. Noise elimination is an important and challenging task in seismic signal processing. Considering the high amplitude continuity of seismic events in the shot domain, this article proposes a structure-oriented denoising method that can enhance the effective events and suppress disturbing noise, including both incoherent and coherent noise. Based on the common-reflection-surface (CRS) travel time, the local slope of seismic events in the shot domain is deduced and estimated to provide structural information for plane-wave prediction. The proposed CRS-based slope depends on fewer parameters (two in 2-D) than the conventional full CRS travel time (three in 2-D), making it computationally efficient. Using the local slope, the third dimension is created using the plane-wave differential equation to predict the current trace from its neighbor traces and trimmed mean filtering (TMF) is applied in this dimension. The added dimension can be regarded as flattening the seismic events within a neighboring window and collapsing after the application of TMF. Synthetic and field datasets are employed to demonstrate the effectiveness of the proposed structure-oriented TMF. Compared with the wavelet and plane-wave destruction (PWD) methods, the proposed method can preserve more useful information with greater continuity in amplitude.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Intelligent MT Data Inversion Method With Seismic Attribute Enhancement

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      Authors: Hongyu Zhou;Rui Guo;Maokun Li;Fan Yang;Shenheng Xu;Maoshan Chen;Yongtao Wang;Deqiang Tao;Zuzhi Hu;Xianwen Cui;Qinian Wang;Jiangbo Zhu;Suhe Huang;
      Pages: 1 - 14
      Abstract: Magnetotelluric (MT) data inversion reconstructs an electrical resistivity structure most compatible with the observed MT data, and static correction can remove the undesired static shift effect in MT data. Conventional MT data static shift correction often faces the challenge of demanding requirements, such as large data amount, additional types of data, or a deep understanding of the research area. MT inversion constrained by seismic data often has better resolution and model consistency compared with independent MT inversion. However, valuable inversion knowledge contained in geophysicists’ expertise is not effectively incorporated. In this work, we present an intelligent MT data inversion method leveraging data- and physics-driven techniques based on deep learning. A novel MT data static shift correction method is introduced based on a neural network (NN). An MT data inversion method is formulated with the constraint of the extracted seismic reflection image based on two different NNs. Experiments on synthetic and field data verify the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Modeling the Effect of Multiscale Heterogeneities on Wave Attenuation and
           Velocity Dispersion

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      Authors: Yurong Wang;Zhaoyun Zong;Qianhao Sun;
      Pages: 1 - 17
      Abstract: Wave attenuation and velocity dispersion are of great significance to fluid evaluation in fluid-saturated rocks. Inspired by the double-porosity and cracked porous elastic wave theory, we develop a multiscale heterogeneities elastic wave theory to better describe the attenuation characteristics caused by wave-induced fluid flow at various scales in saturated rocks. First, we introduce penny-shaped and narrow-shaped coupled cracks into double-porosity model. The potential energy function is given by the stress–strain relationship. The kinetic energy function and dissipation function are derived based on the generalized Biot’s theory. Next, we derive the multiscale wave equations through the Lagrange equation and obtain three compressional waves and one shear wave. According to the novel wave equations, we work out the phase velocity and inverse quality factor in double-porosity and cracked media based on plane-wave analysis. The numerical simulation results show that there are four attenuation peaks in the whole frequency band, corresponding to mesoscopic fluid flow, and two kinds of squirt flow and Biot flow, respectively. Then, we investigate the effect of poroelastic parameters on wave propagation. It is found that porosity, inclusion size, crack aspect ratio, and other parameters significantly affect the dispersion and attenuation. Finally, we compare experimental data and theoretical prediction. Moreover, the new elastic wave theory can degenerate into classical Biot’s theory, double-porosity theory, and pore-crack theory under certain conditions, which demonstrates the consistency of this method. The proposed theory can better predict and simulate the wave propagation by incorporating the effects of microscopic, mesoscopic, and macroscopic heterogeneities.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Imaging Steeply Dipping Faults Using Angle-Controlled Decoupled Elastic
           Reverse-Time Migration of Multicomponent Seismic Data

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      Authors: Hao Hu;Yingcai Zheng;Lianjie Huang;Kai Gao;
      Pages: 1 - 8
      Abstract: High-angle and steeply dipping faults are important for geothermal fluid flow and production. However, imaging such faults is challenging for conventional seismic imaging methods using acoustic waves because of the limited illumination of compressional waves. We develop a novel imaging method to use elastic waves including both compressional-to-compressional (P–P) waves and compressional-to-shear (P-to-S) converted waves to image the high-angle faults. P–S converted waves provide additional seismic illumination for high-angle faults because they have smaller reflection angles than those of the P–P reflection waves at interfaces. Our new imaging method employs the decoupled wave equations in the context of elastic reverse-time migration (ERTM) to perform imaging using both P–P and P–S waves. Our decoupled ERTM does not require the polarity correction of S waves. In addition, we use the Poynting vector, measuring the wave propagation direction by computing the wave energy flux to control the image dip angles, thus improving the ability to image high-angle faults. Based on the angle-controlled P–P and P–S images, we further enhance the structural images of high-angle faults by extracting the significant structures that coexist in both P–P and P–S images. We apply our angle-controlled decoupled ERTM angle-controlled decoupled elastic reverse-time migration (ADEM) to synthetic multicomponent seismic data for a simple layer model with two vertical faults and a modified Marmousi2 model with artificially embedded vertical faults. Our numerical results demonstrate that our ADEM method can effectively image high-angle faults in complex structures using both P–P and P–S waves.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Weakly Supervised Transfer Learning Approach for Radar Sounder Data
           Segmentation

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      Authors: Miguel Hoyo García;Elena Donini;Francesca Bovolo;
      Pages: 1 - 18
      Abstract: Airborne radar sounders (RSs) are active sensors that acquire subsurface data for Earth observation. RS data (radargrams) provide information on buried geology by imaging subsurface dielectric discontinuities. Recently, several automatic RS target identification techniques have been proposed, with convolutional neural network (CNN)-based methods being the most promising. However, they require numerous labeled data that are hard to retrieve in the subsurface environment targeted by RS. Furthermore, they are not designed to effectively deal with problems showing unbalanced classes, such as RS segmentation. We introduce newer cryosphere subsurface targets in the inland and coastal areas that can have a very low probability. To deal with the higher complexity and variability than previous works, we propose a transfer learning framework for RS data to mitigate the need for a large amount of labeled data and handle extremely unbalanced target classes. Herewith, we propose two transfer learning-based mechanisms for radargram segmentation. The first uses a lightweight architecture whose pretraining is supervised with a large labeled dataset from other domains. The second mechanism uses a deep architecture pretrained in the RS domain, considering the pretest task of radargram reconstruction. The architectures are modified to deal with the characteristics of RS data and the radargram segmentation task. Finally, both methods are fine-tuned with a few labeled radargrams to learn radargram features useful for segmentation. We reveal experimental results on radargrams acquired in Antarctica by MCoRDS-1 and MCoRDS-3. The results demonstrate the effectiveness of transfer learning for radargram segmentation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Gravity and Magnetic Focusing Inversion in Revealing the Metallogenic
           Pattern of Dahongshan Copper–Iron Deposit in the Kangdian Area, China

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      Authors: Jun Li;Zhengwei Xu;Xingxiang Jian;Maoru Li;Jinxi Li;Xuben Wang;
      Pages: 1 - 10
      Abstract: The Dahongshan deposit is influenced by pronounced structural controls and intrusions, and its model of mineralization resulting from volcanic sedimentation has faced criticism for an extended period. To describe the deep structure and distribution characteristics of the ore deposit, and to investigate its ore-forming process and metallogenic model, this study uses the gravity, magnetic, and controlled source audio-magnetotelluric (CSAMT) data with different scales to independently recover density, magnetization intensity, and electrical resistivity for shedding light on the deep structure and mineralization distribution of the deposit. The inversion results show the presence of a giant intrusion extending up to 6 km deep within the deposit. The distribution of iron-rich ore bodies and deposit is controlled by the basement tilting and faults, with the deposit exhibiting a U-shaped distribution and the mineral body occurring in a lens-like shape. In addition, the electrical results indicate the presence of high-resistance magma along faults that intrude the deposit. We propose that the deposit is a magmatic-related deposit, with a deep intrusion believed to be the residual source body from early rift intrusion, providing the source for mineralization of the deposit. The characteristics of the deposit controlled by east–west and north–south structures and the lenticular orebody distribution indicate that the deposit is closely influenced by regional structure and magmatism of fault intrusion. The mineralization model is proposed to be a result of the coordinated actions of structural, magmatic, and regional dynamic background for the breakup and convergence of supercontinents.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Road Extraction With Satellite Images and Partial Road Maps

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      Authors: Qianxiong Xu;Cheng Long;Liang Yu;Chen Zhang;
      Pages: 1 - 14
      Abstract: Road extraction is a process of automatically generating road maps mainly from satellite images. Existing models all target to generate roads from the scratch despite that a large number of road maps, though incomplete, are publicly available [e.g., those from OpenStreetMap (OSM)] and can help with road extraction. In this article, we propose to conduct road extraction based on satellite images and partial road maps, which is new. We then propose a two-branch partial-to-complete network (P2CNet) for the task, which has two prominent components: gated self-attention module (GSAM) and missing part (MP) loss. GSAM leverages a channelwise self-attention module and a gate module to capture long-range semantics, filter out useless information, and better fuse the features from two branches. An MP loss is derived from the partial road maps, trying to give more attention to the road pixels that do not exist in partial road maps. Extensive experiments are conducted to demonstrate the effectiveness of our model, e.g., P2CNet achieves the state-of-the-art performance with the intersection over union (IoU) scores of 70.71% and 75.52%, respectively, on the SpaceNet and OSM datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Characterizing Microearthquakes Induced by Hydraulic Fracturing With
           Hybrid Borehole DAS and Three-Component Geophone Data

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      Authors: Zhendong Zhang;Malcolm C. A. White;Tong Bai;Hongrui Qiu;Nori Nakata;
      Pages: 1 - 15
      Abstract: Fluids injected during hydraulic fracturing (fracking) in unconventional shale oil and gas reservoirs, geothermal system enhancement, wastewater disposal, and carbon capture and storage can induce microearthquakes. The spatiotemporal distribution of induced earthquakes is often used to trace the growth of fractures in target layers and guides production. We analyze microseismicity behavior induced by fracking in the Montney Formation, one of the largest unconventional oil and gas reservoirs in North America. An optical fiber deployed in a horizontal well provides extensive spatial sampling and data coverage for microseismic imaging. We design median and F-k filters to predict instrumental and random noise and further suppress them by adaptive noise subtraction. An elliptical vertical transverse isotropic (VTI) velocity model is derived from the Backus-averaged well-log sonic data and is modified to match the microseismic wavefronts by a grid search. We image 41 previously cataloged microearthquakes recorded by distributed acoustic sensing (DAS) using geometric-mean reverse time migration. We find that the fiber geometry’s lack of 2-D/3-D variations increases the nonuniqueness of the image point location, and the $P$ -wave particle motions derived from three-component (3C) geophones data can effectively eliminate the location ambiguity. The spatiotemporal distribution of our updated locations agrees with the fracking schedule. Predicted $P$ - and $S$ -wave travel times from the updated locations also match with the observed waveforms. Analyzing data sensitivity to source locations confirms the potential limitations imposed on source imaging by the geometry of borehole observations and shows that relocation accuracy is directionally dep-ndent. We also investigate the feasibility of estimating source focal mechanisms using realistic DAS and geophone observations. Our study provides guidance for characterizing microearthquake sources and optimizing observation geometry for unconventional reservoirs.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Indicator Spectral Bands and Logistic Models for Detecting Diesel and
           Gasoline Polluted Soils Based on Close-Range Hyperspectral Image Data

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      Authors: Jihee Seo;Jaehyung Yu;Lei Wang;
      Pages: 1 - 13
      Abstract: In this article, we derived indicator spectral bands and classification models for detecting diesel or gasoline pollution in soil using a near- and short-wave-infrared (NIR–SWIR) hyperspectral camera under a close range and laboratory conditions. The soil samples were collected from temperate climate soil with spectral characteristics manifested by secondary minerals. The hyperspectral images show that the diesel and gasoline-polluted soil samples have distinctive spectral differences from clean soil. Different from moisture soil, the spectral absorption features of petroleum hydrocarbons (PHCs) are preserved with an increase in gravimetric content. The more PHCs contents, the stronger the depths at the spectral absorption features. In diesel-polluted soils, the absorption features were observed at various content levels. However, we found a detection limit for gasoline content in the soil, because the absorption features by PHCs disappeared at 8 wt%. To derive the indicator bands, the images were classified by the random forest (RF) algorithm with an accuracy and kappa coefficient of 94.3% and 0.92% using three groups of bands corresponding to ferric ions, C–H stretch/bending, and benzene, toluene, ethylbenzene, and xylene (BTEX) C–H absorptions. The detection models derived from a logistic regression achieved an overall accuracy of 91.82%. The field test of the models on unprocessed soils achieved an accuracy of 83.36%. Because of their simple forms, the logistic detection models can be transferred to remote sensing applications of soil PHC pollution under close-range conditions such as drone-based projects.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multilayer Perceptron and Bayesian Neural Network-Based Elastic Implicit
           Full Waveform Inversion

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      Authors: Tianze Zhang;Jian Sun;Daniel Trad;Kristopher Innanen;
      Pages: 1 - 16
      Abstract: We introduce and analyze the elastic implicit full waveform inversion (EIFWI) of seismic data, which uses neural networks to generate elastic models and perform full waveform inversion. EIFWI carries out inversion by linking two main networks: a neural network that generates elastic models and a recurrent neural network to perform the modeling. The approach is distinct from conventional waveform inversion in two key ways. First, it reduces reliance on accurate initial models relative to conventional FWI. Instead, it invokes general information about the target area, for instance, estimates of means and standard deviations of medium properties in the target area or, alternatively, well-log information in the target area. Second, iterative updating directly affects the weights in the neural network rather than the elastic model. Elastic models can be generated in the first part of the EIFWI process in either of two ways: through the use of a multilayer perceptron (MLP) network or a Bayesian neural network (BNN). Numerical testing is suggestive that the MLP-based EIFWI approach in principle builds accurate models in the absence of an explicit initial model, and the BNN-based EIFWI can give the uncertainty analysis for the prediction results.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Extraction of Multiple Electrical Parameters From IP-Affected Transient
           Electromagnetic Data Based on LSTM-ResNet

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      Authors: Shun Zhang;Nannan Zhou;
      Pages: 1 - 14
      Abstract: The induced polarization (IP) effect due to a polarizable body distorts transient electromagnetic (TEM) data, thereby potentially triggering sign reversal phenomena in the measured response. The measured horizontal electric field associated with a grounded-wire TEM is more strongly affected by IP effects than the measured vertical field, meaning that data inversion is more problematic for its component. The traditional inversion method, which assumes a frequency-independent resistivity, is complex to extract the chargeability. Yet, the chargeability provides critical information, so it is important to extract the chargeability in addition to the resistivity from IP-affected TEM data. Thus, we proposed a data-driven method based on deep learning (DL) to recover the resistivity and chargeability of IP-affected horizontal electric fields. This method, named LSTM-ResNet, combines long short-term memory (LSTM) and a residual network (ResNet) to estimate subsurface electrical properties. Synthetic tests showed that LSTM-ResNet is computationally efficient and accurate for inversion problems. Based on the inverse results with data added noise, we found that a well-trained neural network was not sensitive to noise. A case study was performed by applying LSTM-ResNet to field data collected by a grounded-wire TEM survey at the Kalatongke copper–nickel ore deposit. LSTM-ResNet recovered the simultaneous resistivity and chargeability distributions of subsurface structures from the IP-affected horizontal electric TEM field. The results show a high-chargeability and low-resistivity layer, which was consistent with the lithologic profiles based on drilling cores, indicating the accuracy and robustness of the proposed framework for multiparameter inversion.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Cross-Domain Self-Taught Network for Few-Shot Hyperspectral Image
           Classification

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      Authors: Mingyang Zhang;Hao Liu;Maoguo Gong;Hao Li;Yue Wu;Xiangming Jiang;
      Pages: 1 - 19
      Abstract: In recent years, deep learning models, which possess powerful feature extraction abilities, have achieved remarkable success in the classification of hyperspectral images (HSIs). Nevertheless, a common challenge faced by most deep learning models, including few-shot learning (FSL) models, is the scarcity of valid labeled samples. To address this issue, we propose a cross-domain self-taught network (CDSTN) for few-shot HSI classification. The proposed CDSTN merges domain adaptation (DA) and semisupervised self-taught strategy to implement the FSL, which utilizes adequate labeled and unlabeled samples from source as well as target domains, respectively. For the feature information extraction of HSI, we propose a deep spatial–spectral feature embedded extractor composed of four residual blocks and a channel attention module (CAM). Additionally, a set of domain classifiers are introduced behind each residual block for the purpose of domain alignment by extracting more domain information at different depths of the network. Finally, plenty of unlabeled samples are assigned with pseudo labels through the trained network, and a pseudo label refinement (PLR) module is designed to select the most confident pseudo label sample for each class to further enrich the labeled database of target domain. Experiments conducted on four widely used benchmark HSI datasets demonstrate that CDSTN can obtain superior and stable performance with limited labeled samples compared with some state of the arts.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Incorporating the Nearly Constant Q Models Into 3-D Poro-Viscoelastic
           Anisotropic Wave Modeling

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      Authors: Li Han;Xingguo Huang;Qi Hao;Stewart Greenhalgh;Xu Liu;
      Pages: 1 - 11
      Abstract: The Earth is often characterized by viscoelastic rocks, porous sediments, and anisotropic structures. Poro-elasticity with Biot’s theory is considered fundamental to describe the interaction between the deformation of the elastic porous solid and the flow of fluid in the porous structure. The quality factor ( $Q$ ) in the theory of viscoelasticity relates seismic wave attenuation and dispersion to physical properties of the Earth’s interior, e.g., temperature, stress, and composition. However, the constant $Q$ wave equation in its time-domain differential form remains difficult to solve when describing the attenuation in an explicitly specified $Q$ parameter. Here, we introduce the first- and second-order nearly constant $Q$ models capable of describing the attenuation of the solid skeleton, thereby extending the Biot and Biot-squirt (BISQ) models to poro-viscoelastic media. The bulk and shear moduli of the solid frame are represented by the modified relaxation function. By presenting examples with finite-difference time-domain (FDTD) numerical modeling for seismic wavefields in anisotropic, viscoelastic porous media including transversely isotropic media with a vertical symmetry axis [vertical transverse isotropy (VTI)] and orthorhombic media, we demonstrate that the extended Biot and BISQ models provide good descriptions of the wave propagation in poro-viscoelastic anisotropic media and can thus help better understand the Earth’s interior.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Exact Zoeppritz Based Prestack Inversion Using Whale Optimization
           Particle Filter Algorithm Under Bayesian Framework

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      Authors: Jing Tang;Peng Li;Xuri Huang;Qiang Lai;Ding Wang;
      Pages: 1 - 10
      Abstract: Conventional amplitude versus offset (AVO) inversion methods are mainly based on various Zoeppritz approximations. The assumptions of small contrast and linear relationship lead to the most inversion methods being difficult to have high inversion accuracy. In this article, the exact Zoeppritz equation is used to establish the prestack inversion method under the Bayesian framework. It integrates multisource information to generate posterior distributions of P-, S-wave velocity and density. In the Bayesian theory, the prior model works as the regularization term which has a strong effect on the inversion results. The strategy to obtain a relatively accurate prior model can improve the inversion accuracy. Therefore, an exact Zoeppritz equation based nonlinear AVO inversion algorithm combing whale optimization particle filtering (WOPF) is proposed. The WOPF method can generate a relatively stable and accurate initial model for the Bayesian prestack inversion. We validate the new method through two synthetic models and field data. Comparisons are made with the conventional linear and nonlinear AVO inversion methods. The results show that the proposed method can provide much more accurate inverted elastic parameters in different geological conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Adaptive Focused Beam Prestack Depth Migration Under the Condition of
           Rugged Topography

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      Authors: Jianguang Han;Qingtian Lü;Bingluo Gu;Zhantao Xing;
      Pages: 1 - 8
      Abstract: Complex topography is a challenging issue in onshore seismic exploration. The rugged terrain and lateral change of near-surface velocity pose a significant obstacle to the accurate imaging of seismic data. The adaptive focused beam migration method retains the good applicability of the ray methods for calculating the seismic wavefield under complex surface conditions. It can effectively solve the contradiction between the imaging accuracy of deep and shallow strata in traditional Gaussian beam migration. We extend the adaptive focused beam migration approach to the deep domain imaging of seismic data under complex surface conditions. First, the basic principles of the adaptive focused beam are reviewed. Then, Green’s functions of the seismic source and the receiving point of rugged topography are characterized by the adaptive focused beam, and an adaptive focused beam prestack depth migration method based on cross correlation imaging is proposed. The full-wave-arrival imaging strategy is applied to image all wave arrivals of subsurface imaging points. A single input seismic trace is adopted for imaging, which can directly emit the focused beam from the receiving point of rugged topography for wave field continuation, thus avoiding multiple focusing. As a result, the applicability of the migration approach to complex surface conditions was improved, and the imaging accuracy of the migration method was also effectively enhanced. The numerical model migration test of different rugged topography conditions and tectonic forms verified that the proposed method was an effective prestack depth migration applicable to accurately imaging seismic data under the rugged topography condition.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Slice-Relation-Clustering Framework via Horizontal Angle Information for
           3-D Tree Roots Reconstruction

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      Authors: Wenhao Luo;Yee Hui Lee;Lai Fern Ow;Mohamed Lokman Mohd Yusof;Abdulkadir C. Yucel;
      Pages: 1 - 10
      Abstract: Tree root system 3-D reconstruction and spatial distribution analysis are the prevalent aspects of tree root investigation using ground penetrating radar (GPR). Precedent 3-D reconstruction methods are found to be effective in mapping simple, smooth root structures. However, repetitive and dense B-scans are needed; otherwise, the retrieved roots’ spatial distribution and growth extension trend accuracy would deteriorate with the increase in the root systems’ complexity. To address these issues, this article, for the first time, explores the possibility of integrating the horizontal angle information of the tree roots and a slice-relation-clustering (SRC) algorithm to reconstruct the complex tree root systems in a 3-D manner. The proposed framework, which takes the roots’ horizontal angle as an analyzing condition instead of biological properties that are similar among neighboring branches used in the existing methods, clusters preprocessed and focused 2-D reflection patterns from the same single root together. The whole roots system is the combination of every single root cluster. Real measurement results show that our proposed method achieves a high efficiency in accurate root system reconstruction.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Inversion of Time-Lapse Surface Gravity Data for Detection of 3-D CO2
           Plumes via Deep Learning

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      Authors: Adrian Celaya;Bertrand Denel;Yen Sun;Mauricio Araya-Polo;Antony Price;
      Pages: 1 - 11
      Abstract: We introduce two algorithms that invert simulated gravity data to 3-D subsurface rock/flow properties. The first algorithm is data-driven, deep learning (DL)-based approach, and the second is also data-driven but considers the temporal evolution of surface gravity events. The target application of these proposed algorithms is the prediction of subsurface CO2 plumes as a complementary tool for monitoring CO2 sequestration deployments. Each proposed algorithm outperforms traditional inversion methods and produces high-resolution, 3-D subsurface reconstructions in near real-time. In addition, our proposed methods achieve Dice scores of up to 0.8 for predicted plume geometry and near-perfect data misfit in terms of $mu $ Gals. These results indicate that combining 4-D surface gravity monitoring (low-cost acquisition) with DL techniques represents an effective and nonintrusive method for monitoring CO2 storage sites.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Negation Invariant Representations of 3-D Vectors for Deep Learning Models
           Applied to Fault Geometry Mapping in 3-D Seismic Reflection Data

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      Authors: Daniel Kluvanec;Kenneth J. W. McCaffrey;Thomas B. Phillips;Noura Al Moubayed;
      Pages: 1 - 16
      Abstract: We can represent the orientation of a plane in 3-D by its normal vector. However, every plane has two normal vectors that are negatives of each other. We propose four novel representations of vectors in 3-D that are negation invariant and can be used by a neural network to predict orientation. Our proposed solution is the first to introduce representations that are negation invariant, continuous, and easily parallelizable on the graphics processing unit (GPU). We evaluate the representations by predicting the orientation of a plane on a toy task, and by applying them to synthetic seismic tomographic data where we predict the presence and orientation of faults for every voxel in the volume. We further make use of the orientation of the faults in a post-processing algorithm on the GPU that separates the faults into segments (i.e., instances) that do not intersect, which allows us to selectively visualize faults in 3-D. We demonstrate the utility of the representations by deploying the model on the Laminaria 3-D seismic volume as a case study. We quantitatively compare the model’s prediction against human interpretations of slices through the volume as well as existing interpretations in literature. Our analysis shows good agreement (F1 score of 88%) of the model with human interpretation in the shallow levels, where the ambient noise is lower, but this agreement degrades at deeper levels (F1 score of 68%). We explore possible reasons for this degradation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Inversion of the Gravity Gradiometry Data by ResUnet Network: An
           Application in Nordkapp Basin, Barents Sea

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      Authors: Zhengwei Xu;Rui Wang;Michael S. Zhdanov;Xuben Wang;Jun Li;Bing Zhang;Shengxian Liang;Yang Wang;
      Pages: 1 - 10
      Abstract: The study and assessment of the subsurface density distribution are vital for mining and oil and gas exploration. This can be achieved by the 3-D inversion of the observed gravity and gravity gradiometry (GG) data. Due to the ill-posedness of the geophysical inverse problem, the nonuniqueness and instability of solutions represent the main difficulties in inversion. In recent years, convolutional neural networks, especially U-net technology, have found wide applications in image processing, recognition, and reconstruction. This article proposes using this method for fast reconstruction of the subsurface density models based on the ResUnet technology. The developed new method was examined on two 3-D synthetic gravity and GG datasets inversion. The results show that the ResUnet network can reconstruct the density anomaly with sharp boundaries and is robust to the noise, making the solution stable.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • RockFormer: A U-Shaped Transformer Network for Martian Rock Segmentation

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      Authors: Haiqiang Liu;Meibao Yao;Xueming Xiao;Yonggang Xiong;
      Pages: 1 - 16
      Abstract: Martian rock segmentation aims to separate rock pixels from background, which plays a crucial role in downstream tasks, such as traversing and geologic analysis by Mars rovers. The U-Nets have achieved certain results in rock segmentation. However, due to the inherent locality of convolution operations, U-Nets are inadequate in modeling global context and long-range spatial dependencies. Although emerging Transformers can solve this, they suffer from difficulties in extracting and retaining sufficient low-level local information. These shortcomings limit the performance of the existing networks for Martian rocks that are variable in shape, size, texture, and color. Therefore, we propose RockFormer, the first U-shaped Transformer framework for Mars rock segmentation, consisting of a hierarchical encoder–decoder architecture with a feature refining module (FRM) connected between them. Specifically, the encoder hierarchically generates multiscale features using an improved vision Transformer (improved-ViT), where both abundant local information and long-range contexts are exploited. The FRM removes less representative features and captures global dependencies between multiscale features, improving RockFormer’s robustness to Martian rocks with diverse appearances. The decoder is responsible for aggregating these features for pixelwise rock prediction. For evaluation, we establish two Mars rock datasets, including both real and synthesized images. One is MarsData-V2, an extension of our previously published MarsData collected from real Mars rocks. The other is SynMars, a synthetic dataset sequentially photographed from a virtual terrain built referring to the TianWen-1 dataset. Extensive experiments on the two datasets show the superiority of RockFormer for Martian rock segmentation, achieving state-of-the-art performance with decent computational simplicity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Analysis of Lava Tubes’ Roughness and Radar Near-Nadir Regime
           Backscattering Properties

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      Authors: Leonardo Carrer;Lorenzo Bruzzone;
      Pages: 1 - 13
      Abstract: Lava tubes are terrestrial tunnel-like natural subsurface caves. Mounting evidence suggests their presence on the Moon and Mars. Planetary radar sounders are nadir-looking instruments operating in the high-frequency (HF)/very-HF (VHF) part of the spectrum with subsurface penetration capabilities. Recently, several studies either proposed future mission concepts for lava tubes’ detection or attempted to locate them on the Moon and Mars with the available radar-sounding data. Lava tubes are typically modeled as quasi-cylindrical structures but their actual geometry and their influence on the radar backscattering in near-nadir regime have never been investigated in the literature. These are crucial information for understanding the feasibility of detecting lava tubes by current and future planetary radar sounding systems. Accordingly, in this article: 1) we assess whether lava tubes are self-affine fractal surfaces at horizontal scales relevant to radio and microwave scattering and 2) we evaluate the effect of lava tube topography on the radar backscattering response in the near-nadir regime. Our experimental results, which are inferred from 3-D terrestrial laser scanning (TLS) data of planetary lava tube analogs, show that lava tubes: 1) are self-affine fractals at horizontal scales relevant to radar sounding and 2) they are electromagnetically rough surfaces, especially in the VHF band. We provide quantitative values on the lava tube fractal parameters and radar roughness losses along with a discussion on both: 1) the implication of our results on current radar sounding systems’ ability to detect lava tubes and 2) the planning of future missions devoted to lava tube detection and characterization.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Threshold Attention Network for Semantic Segmentation of Remote Sensing
           Images

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      Authors: Wei Long;Yongjun Zhang;Zhongwei Cui;Yujie Xu;Xuexue Zhang;
      Pages: 1 - 12
      Abstract: Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-range pixel dependencies. SA enables the network to model the global dependencies between the input features, resulting in improved segmentation outcomes. However, the high density of attentional feature maps used in this mechanism causes exponential increases in computational complexity. In addition, it introduces redundant information that negatively impacts the feature representation. Inspired by traditional threshold segmentation algorithms, we propose a novel threshold attention mechanism (TAM). This mechanism significantly reduces computational effort while also better modeling the correlation between different regions of the feature map. Based on TAM, we present a threshold attention network (TANet) for semantic segmentation. The TANet consists of an attentional feature enhancement module (AFEM) for global feature enhancement of shallow features and a threshold attention pyramid pooling (TAPP) module for acquiring feature information at different scales for deep features. We have conducted extensive experiments on the international society for photogrammetry and remote sensing (ISPRS) Vaihingen and Potsdam datasets. The results demonstrate the validity and superiority of our proposed TANet compared with most state-of-the-art models.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Level-Aware Consistent Multilevel Map Translation From Satellite Imagery

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      Authors: Ying Fu;Zheng Fang;Linwei Chen;Tao Song;Defu Lin;
      Pages: 1 - 14
      Abstract: With the rapid development of remote sensing technology, the quality of satellite imagery (SI) is getting higher, which contains rich cartographic information that can be translated into maps. However, existing methods either only focus on generating single-level map or do not fully consider the challenges of multilevel translation from satellite imageries, i.e., the large domain gap, level-dependent content differences, and main content consistency. In this article, we propose a novel level-aware fusion network for the SI-based multilevel map generation (MLMG) task. It aims to tackle these three challenges. To deal with the large domain gap, we propose to generate maps in a coarse-to-fine way. To well-handle the level-dependent content differences, we design a level classifier to explore different levels of the map. Besides, we use a map element extractor to extract the major geographic element features from satellite imageries, which is helpful to keep the main content consistency. Next, we design a multilevel fusion generator to generate a consistent multilevel map from the multilevel preliminary map, which further ensures the main content consistency. In addition, we collect a high-quality multilevel dataset for SI-based MLMG. Experimental results show that the proposed method can provide substantial improvements over the state-of-the-art alternatives in terms of both objective metric and visual quality.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Easy-to-Hard Structure for Remote Sensing Scene Classification in
           Multitarget Domain Adaptation

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      Authors: Ba Hung Ngo;Yeon Jeong Chae;Jae Hyeon Park;Ju Hyun Kim;Sung In Cho;
      Pages: 1 - 15
      Abstract: Multitarget domain adaptation (MTDA) is a transfer learning task that uses knowledge extracted from a labeled source domain to adapt across multiple unlabeled target domains. The MTDA setting is more complicated than the single-source-single-target domain adaptation (S3TDA) setting because domain shift not only exists in each pair of a source–target domain but also exists among different target domains. In addition, multiple-target domains have their own unique characteristics because they are often collected from various conditions. The semantic information in each target domain can be damaged when they are naïvely merged into a single-target domain. Therefore, the trained model struggles to distinguish between representations in the combined target domain, which degrades the classification performance. Furthermore, the knowledge transferability from the source domain to multiple-target domains in prior studies leaves room for improvement because they only focus on exploiting the relationship of source–target pairs while failing to consider the correlation among multiple-target domains. This article introduces an easy-to-hard adaption structure to solve these problems in MTDA. The proposed method consists of three components: Extracting source representations, Hierarchical intratarget feature Alignment, and Collaborative intertarget feature Alignment, called EHACA. These components are used to encode the semantic information in each target domain and explore the relationships between the source and target domains, and among different target domains. The proposed method shows outstanding classification performance over five remote sensing datasets of MTDA tasks, surpassing state-of-the-art approaches in most experimental scenarios.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A CNN-Based Sentinel-2 Image Super-Resolution Method Using Multiobjective
           Training

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      Authors: Vlad Vasilescu;Mihai Datcu;Daniela Faur;
      Pages: 1 - 14
      Abstract: Deep learning methods have become ubiquitous tools in many Earth observation applications, delivering state-of-the-art results while proving to generalize for a variety of scenarios. One such domain concerns the Sentinel-2 (S2) satellite mission, which provides multispectral images in the form of 13 spectral bands, captured at three different spatial resolutions: 10, 20, and 60 m. This research aims to provide a super-resolution mechanism based on fully convolutional neural networks (CNNs) for upsampling the low-resolution (LR) spectral bands of S2 up to 10-m spatial resolution. Our approach is centered on attaining good performance with respect to two main properties: consistency and synthesis. While the synthesis evaluation, also known as Wald’s protocol, has spoken for the performance of almost all previously introduced methods, the consistency property has been overlooked as a viable evaluation procedure. Recently introduced techniques make use of sensor’s modulation transfer function (MTF) to learn an approximate inverse mapping from LR to high-resolution images, which is on a direct path for achieving a good consistency value. To this end, we propose a multiobjective loss for training our architectures, including an MTF-based mechanism, a direct input–output mapping using synthetically degraded data, along with direct similarity measures between high-frequency details from already available 10-m bands, and super-resolved images. Experiments indicate that our method is able to achieve a good tradeoff between consistency and synthesis properties, along with competitive visual quality results.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Accurate Label Refinement From Multiannotator of Remote Sensing Data

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      Authors: Xiangyu Wang;Lyuzhou Chen;Taiyu Ban;Derui Lyu;Yifeng Guan;Xingyu Wu;Xiren Zhou;Huanhuan Chen;
      Pages: 1 - 13
      Abstract: The remote sensing (RS) field has an increasing research interest in using deep learning (DL) models to recognize kinds of RS data, leading to a great demand for training data annotation. Due to the high cost of expertise, using nonexperts to label data has become an important way to improve labeling efficiency. Commonly, a single data sample is labeled by multiple annotators and the most voted label is accepted to promise accuracy. But in the RS context, the widely admitted strategy could lose effect. Usually RS data involve considerable classes on account of the complexity of surface environments, which is prone to interclass similarity difficult to distinguish. Annotators without expertise probably make mistakes on these indistinguishable classes, thus causing error voted labels. Although classification of different characteristics in RS data has been widely documented, the nonexpert annotators are unfamiliar with these expertise, and it is difficult to force them to handle specialized labeling skills. To address the issues, this article bases multiannotator label selection on the investigation of annotators’ own ability in distinguishing similar classes of images. A quality evaluation process is designed which weights the labels from capable annotators higher than those from weak ones. By a multi-round quality evaluation algorithm, correct labels could outcompete the wrong ones even disadvantaged in numbers. Experimental results demonstrate the advance of the proposed method on the RS datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Universal Domain Adaptation for Remote Sensing Image Scene Classification

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      Authors: Qingsong Xu;Yilei Shi;Xin Yuan;Xiao Xiang Zhu;
      Pages: 1 - 15
      Abstract: The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios of remote sensing image classification since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are often not accessible due to privacy or confidentiality issues. To this end, we propose a practical universal DA (UniDA) setting for remote sensing image scene classification that requires no prior knowledge on the label sets. Furthermore, a novel UniDA method without source data is proposed for cases when the source data are unavailable. The architecture of the model is divided into two parts: the source data generation stage and the model adaptation stage. The first stage estimates the conditional distribution of source data from the pretrained model using the knowledge of class separability in the source domain and then synthesizes the source data. With this synthetic source data in hand, it becomes a UniDA task to classify a target sample correctly if it belongs to any category in the source label set or mark it as “unknown” otherwise. In the second stage, a novel transferable weight that distinguishes the shared and private label sets in each domain promotes the adaptation in the automatically discovered shared label set and recognizes the “unknown” samples successfully. Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data are available or not. The code is available at https://github.com/zhu-xlab/UniDA.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Generative Building Feature Estimation From Satellite Images

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      Authors: Liu He;Jie Shan;Daniel Aliaga;
      Pages: 1 - 13
      Abstract: Urban and environmental researchers seek to obtain building features (e.g., building shapes, counts, and areas) at large scales. However, blurriness, occlusions, and noise from prevailing satellite images severely hinder the performance of image segmentation, super-resolution, or deep-learning-based translation networks. In this article, we combine globally available satellite images and spatial geometric feature datasets to create a generative modeling framework that enables obtaining significantly improved accuracy in per-building feature estimation and the generation of visually plausible building footprints. Our approach is a novel design that compensates for the degradation present in satellite images by using a novel deep network setup that includes segmentation, generative modeling, and adversarial learning for instance-level building features. Our method has proven its robustness through large-scale prototypical experiments covering heterogeneous scenarios from dense urban to sparse rural. Results show better quality over advanced segmentation networks for urban and environmental planning, and show promise for future continental-scale urban applications.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • TCD: Task-Collaborated Detector for Oriented Objects in Remote Sensing
           Images

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      Authors: Caiguang Zhang;Boli Xiong;Xiao Li;Gangyao Kuang;
      Pages: 1 - 14
      Abstract: Oriented object detection (OOD) in remote sensing image interpretation is challenging due to the difficulty of locating objects with arbitrary orientations. Existing methods have made considerable progress based on oriented heads or anchors. However, most of them follow the classical detection paradigm, such as assigning samples based on Intersection-over-Unions (IoU) and predicting through two independent tasks. These fixed strategies impair the consistency between classification and localization predictions, resulting in the prediction with optimal localization accuracy being suppressed by the nonoptimal ones during nonmaximum suppression (NMS). To address this problem, a task-collaborated detector (TCD) is proposed. Compared with current single-stage methods, its improvements include two aspects: task-collaborated assignment (TCA) and task-collaborated head (TCH). Specifically, to better pull closer the best anchors for two tasks, TCA introduces classification and localization confidence into sample assignment and tends to select the anchors with accurate and consistent predictions as positive during training. TCH provides a better balance for learning interactive and discriminative features. It can flexibly adjust the spatial feature distribution of classification and localization tasks by learning the joint features from the aggregation layer. Extensive experiments are conducted on HRSC2016, DOTA, and DIOR-R, and the proposed TCD achieves the state-of-the-art performance [90.60, 80.89, and 65.04 mean average precision (mAP), respectively]. Consistency analysis also demonstrates that TCD can significantly improve prediction consistency.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Physics Guided Remote Sensing Image Synthesis Network for Ship Detection

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      Authors: Weichang Zhang;Rui Zhang;Guoqing Wang;Wei Li;Xun Liu;Yang Yang;Die Hu;
      Pages: 1 - 14
      Abstract: Automatic detection and localization of objects in remote sensing images are of great significance for remote sensing systems. Existing frameworks usually train an object detection network using collected remote sensing images. However, these models usually perform poorly due to the lack of large-scale training datasets, which is often the case for special remote sensing scenarios, e.g., the detection of ships in the open sea. Although image synthesis is a common strategy to alleviate the issue of data insufficiency, the trained model still performs poorly when being tested on real-world scenes. Aimed at this, a novel sensor-related image synthesis framework, dubbed as remote sensing-image synthesis pipeline (RS-ISP), is developed to address the lack of on-orbit remote sensing images. Specifically, our RS-ISP introduces two novel designs to ensure the distribution consistency between the generated images and the real images: 1) the first is a novel pipeline for modeling the physical process of noise production during image capture using specific sensors and 2) the second is the design of a detection-oriented image harmonization model. Similar to the existing design, our model first produces coarse synthetic images by copy–paste operation, on which the proposed harmonization process is used to reduce the variation in the pasted foreground and background. By incorporating these two designs into a unified framework, our RS-ISP is designed and used to produce large-scale synthetic images used to train the object detection model for detecting ships in remote sensing images. Comparative experiments demonstrated that RS-ISP increased the AP@.50 from 0.148 to 0.498 for the ship detection task. Code will be publicly available.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep Saliency Smoothing Hashing for Drone Image Retrieval

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      Authors: Yaxiong Chen;Jinghao Huang;Lichao Mou;Pu Jin;Shengwu Xiong;Xiao Xiang Zhu;
      Pages: 1 - 13
      Abstract: Deep hashing algorithms are widely exploited in retrieval tasks due to their low storage and retrieval efficiency. Most of them focus on global feature learning, while neglecting local fine-grained features and saliency information for drone images. In this article, we tackle these dilemmas with a novel deep saliency smoothing hashing (DSSH) algorithm, which can leverage saliency capture mechanism, distribution smoothing term, global features, and local fine-grained features to learn effective hash codes for drone image retrieval. The DSSH algorithm first designs an information extraction module to capture global features and local fine-grained features for drone images. Meanwhile, a saliency capture module is proposed to perform information interaction attention and visual enhancement attention, which can capture the saliency area of drone images effectively. On top of the two paths, a novel objective function is designed to preserve the similarity of hash codes, smooth the distribution of drone image datasets, and reduce the quantization errors between hash codes and hash-like codes concurrently. Extensive experiments on the Drone Action Dataset and ERA Drone Dataset demonstrate that the DSSH algorithm can further improve the retrieval performance compared with other deep hashing algorithms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Efficient Fine-Grained Object Recognition in High-Resolution Remote
           Sensing Images From Knowledge Distillation to Filter Grafting

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      Authors: Liuqian Wang;Jing Zhang;Jimiao Tian;Jiafeng Li;Li Zhuo;Qi Tian;
      Pages: 1 - 16
      Abstract: With the development of high-resolution remote sensing images (HR-RSIs) and the escalating demand for intelligent analysis, fine-grained recognition of geospatial objects has become a more practical and challenging task. Although deep learning-based object recognition has achieved superior performance, it is inflexible to be directly utilized to the fine-grained object recognition (FGOR) tasks of HR-RSIs under the limitation of the size of geospatial objects. An efficient fine-grained object recognition method in HR-RSIs from knowledge distillation (KL) to filter grafting is proposed. Specifically, fine-grained object recognition consists of two stages: Stage 1 utilizes oriented region convolutional neural network (oriented R-CNN) to accurately locate and preliminarily classify geospatial objects. At the same time, it serves as a teacher network to guide students’ effective learning of fine-grained object recognition; in Stage 2, we design a coarse-to-fine object recognition network (CF-ORNet), as the second teacher network, which realizes fine-grained recognition through feature learning and category correction. After that, we propose a lightweight model from knowledge distillation to filter grafting on two teacher networks to achieve efficient fine-grained object recognition. The experimental results on Vehicle Detection in Aerial Imagery (VEDAI) and HR Ship Collection 2016 (HRSC2016) datasets achieve competitive performance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Self-Supervision Interactive Alignment for Remote Sensing
           Image–Audio Retrieval

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      Authors: Jinghao Huang;Yaxiong Chen;Shengwu Xiong;Xiaoqiang Lu;
      Pages: 1 - 14
      Abstract: Cross-modal remote sensing image–audio (RSIA) retrieval aims to use audio or remote sensing images (RSIs) as queries to retrieve relevant RSIs or corresponding audios. Although many approaches leverage labeled samples to achieve good performance, the performance cost of labeled samples is high, because cross-modal remote sensing (RS) labeled samples usually require huge labor resources. Therefore, unsupervised cross-modal learning is very important in real-world applications. In this article, we propose a novel unsupervised cross-modal RSIA retrieval approach, named self-supervision interactive alignment (SSIA), which can take advantage of large amounts of unlabeled samples to learn the salient information, cross-modal alignment, and the similarity between RSIs and audios. Since self-supervised learning lacks the supervision of label information, we leverage the similarity between the input RSI information and audio information as the supervision information. Besides, to perform cross-modal alignment, a novel interactive alignment (IA) module is designed to explore fine correspondence relation for RSIs and audios. Moreover, we design an audio-guided image de-redundant module to reduce the redundant information of visual information, which can capture salient information of RSIs. Extensive experiments on four widely used RSIA datasets testify that the SSIA performance gains better RSIA retrieval performance than other compared approaches.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Semi-MapGen: Translation of Remote Sensing Image Into Map via
           Semisupervised Adversarial Learning

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      Authors: Jieqiong Song;Hao Chen;Chun Du;Jun Li;
      Pages: 1 - 19
      Abstract: Online maps play an essential role in modern life. The convenience of acquiring remote sensing images provides reliable geographic information sources for the compilation of online maps. Some existing works have used the idea of domain mapping to translate remote sensing images into maps directly, which is of great prospect for application. However, many of the current remote sensing image-to-map translation (RSMT) works are performed in an unsupervised manner, which would lead to problems such as distortion and local detail inaccuracy. Although the fully supervised method is effective, it requires plenty of paired as well as matched data for training. Paired remote sensing images and maps with consistent spatial locations can be easily accessed through online map services, whereas many pairs of samples in which some geographic element information is not accurately and completely matched. Supervised learning-based translation models are often confused by these unmatched data. Accurate and complete matched data have to be selected deliberately by humans, and the manual selection process is time-consuming and laborious, which brings new challenges. Therefore, we propose a novel RSMT model named Semi-MapGen based on semisupervised generative adversarial networks (GANs), which requires only a small set of accurate and complete matched data and plenty of unpaired data. In this model, we apply a knowledge extension-based learning strategy that can improve the accuracy of translated maps. In addition, we design the expansion loss and channelwise loss to learn the information from massive unpaired data in an unsupervised manner. Qualitative and quantitative experiment results on three datasets demonstrate that the proposed model outperforms state-of-the-art semisupervised and supervised methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Improved Method for Water Body Removal in Spaceborne GNSS-R Soil
           Moisture Retrieval

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      Authors: Wentao Yang;Fei Guo;Xiaohong Zhang;Yifan Zhu;
      Pages: 1 - 8
      Abstract: The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this article, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient ( $R$ ) and root-mean-square error (RMSE) of the 36-km grid are 0.50 and 0.057 cm3/cm3, respectively, while the $R$ and RMSE of the 3-km grid are 0.68 and 0.041cm3/cm3, respectively. Such performances are better than the traditional method. Moreover, the method proposed in this article preserves more grids. Take the 3-km spatial resolution, for example, it preserves 2.2-fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this article is 16.3% (8.2% for the traditional method). In the in situ validation, the overall improvement of RMSE is 19.4% (−1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body remo-al method proposed in this article can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep Learning-Based Rainfall Prediction Using Cloud Image Analysis

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      Authors: Jongyun Byun;Changhyun Jun;Jinwon Kim;Jaehoon Cha;Roya Narimani;
      Pages: 1 - 11
      Abstract: This study presents a new research direction for predicting rainfall amount using cloud image data. Herein, we employ a convolutional neural networks (CNNs) to develop an image-value model from cloud image data collected from May 20, 2020 to October 24, 2020 using the Internet of Things (IoT) sensors installed at two research locations in Seoul, Republic of Korea. First, we refine the dataset using data preprocessing in three steps: 1) day/night discrimination; 2) ratio adjustment; and 3) image augmentation. Second, we construct a binary classification model using one-hot encoding for the existence of rainfall. This reduces no-rain data instances and increases model performance, thereby enabling the model to extract image features. Finally, we develop a CNN-based image-value model for rainfall prediction with a well-organized model configuration. Rainfall existence results derived from the binary classification model used for model input as preprocessed cloud image data. The proposed rainfall prediction model exhibited 85.59% accuracy on cloud images with an average mean squared error (MSE) of 3.05 for observation data under 3 mm/h. In particular, single application of the function that divides Boolean input by the standard deviation of the dataset within each characteristic resulted in a 17% increase in predicted rainfall accuracy. To the best of our knowledge, this is the first study to train CNN model to predict value (rainfall) with matched image data (cloud), which could be denoted as CNN-based image-value model. Notably, the proposed model can be further extended into other image datasets, including rain streaks with various backgrounds under different climatic conditions.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Cross-View Image Synthesis From a Single Image With Progressive Parallel
           GAN

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      Authors: Yingying Zhu;Shihai Chen;Xiufan Lu;Jianyong Chen;
      Pages: 1 - 13
      Abstract: Cross-view image synthesis aims to synthesize a ground-view image covering the same geographic region for a given single aerial-view image (or vice versa). Existing approaches typically tackle this challenging task by relaxing the single-image constraint and using a ground-truth semantic map as additional input to aid synthesis. However, this is nearly infeasible in practice. In this article, we investigate how to generate a detail-enriched and structurally accurate ground-level image from only a single aerial-level input image, without any other prior knowledge except for the input image. Toward this goal, we propose a novel progressive parallel generative adversarial network (PPGAN) that starts by generating low-resolution outputs and progressively produces ground images at higher resolutions as the network propagates forward. In this manner, our PPGAN decomposes the task into several manageable sub-tasks, which helps generate detail-enriched and structurally accurate ground images. During progressive generation, the PPGAN uses a parallel generation paradigm that enables the generator to produce multiresolution images in parallel, thereby avoiding excessive time cost on training. Furthermore, for effective information propagation across multiresolution images, a feature fusion module (FFM) is devised to mitigate the domain gap between cross-level image features, which enables a balance of detail and structural information synthesis. In addition, the proposed channel-space attention selection module (CSASM) learns the mapping relationship between cross-view images in a larger scale space to enhance the quality of the output image. Quantitative and qualitative experiments demonstrate that our method requires only one input image without the aid of additional inputs, but is capable of synthesizing detail-enriched and structurally accurate ground images and outperforms the existing state-of-the-art methods on two famous benchmarks.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Remote Sensing Object Counting Through Regression Ensembles and Learning
           to Rank

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      Authors: Yongbo Huang;Yuanpei Jin;Liqiang Zhang;Yishu Liu;
      Pages: 1 - 17
      Abstract: Remote sensing object counting (RSOC) is finding applications in many fields. Global regression is a long-ignored method for object counting, though it needs much less manual annotations than the alternatives. This work revisits global regression and improves it in two ways—one way is by replacing one single regressor with a deep ensemble, and the other is by breaking down global regression into two easier and smaller problems: learning to rank (L2R) and linear transformation (LT). To this end, we make a probably approximately correct (PAC)-Bayesian analysis of regression ensembles and give an upper bound for their generalization error, offering new theoretical insight into ensemble learning. We also adapt a ranking metric optimization scheme to suit object counting, elegantly handling the L2R problem with gradient descent. Furthermore, based on our theoretical perspective, we provide a novel way of building deep regression ensembles, on which the ambiguity constraint is imposed. Then, by incorporating L2R into a deep ensemble, we propose a new counting model called the “ensemble of first-rank-then-estimate networks (eFreeNet).” Our extensive evaluation on six benchmarks shows that the eFreeNet exhibits compelling performance across the board while being more annotation-efficient than other methods. Our source code is publicly available at https://github.com/huangyongbobo/eFreeNet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Time Domain Model for Permittivity and Thickness Measurement

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      Authors: Xianzhong Tian;Tianying Chang;Yongxin Guo;Hong-Liang Cui;
      Pages: 1 - 11
      Abstract: Motivated by the necessity of acquiring wall parameters for through-the-wall radar (TWR), a novel and general time domain model is proposed to measure the thickness and permittivity of single-layered slab-shaped materials, by exploiting the delays of the two surface reflections in the bistatic radar scheme. First, the two surface delays are formulated as functions of the unknown permittivity and thickness, as well as the accessible incident angle, and a nonlinear equation set is formed. Then based on a geometric analysis, in two separate bistatic delay tests with different antenna separations and standoff distances, the condition of identical incident angle is established. As such, the intra-wall delay is the same for the two delay tests, leading to a significant simplification and a closed-form solution to the equation set. Finally, a three-antenna test setup is constructed, with which the desired parameters can be acquired conveniently and accurately by performing bistatic tests at a set of standoff distances. Simulation and experiment show that our method can achieve high accuracy and strong robustness against noise.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • STANet: A Novel Predictive Neural Network for Ground-Based Remote Sensing
           Cloud Image Sequence Extrapolation

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      Authors: Zhiying Lu;Zhiyi Zhou;Xin Li;Jianfeng Zhang;
      Pages: 1 - 11
      Abstract: Cloud image sequence extrapolation plays an important role in ground-based remote sensing observation because allows the observation range to be extended in the spatiotemporal domain. Existing methods, primarily focus on characterizing spatial features and capturing temporal state transitions independently, ignoring the complex spatiotemporal dynamics of the real physical world, resulting in images extrapolated by them being less than expected. To break through this dilemma, we propose the Spatio-Temporal-Aware Network (STANet) for ground-based remote sensing cloud image sequence extrapolation. The method is a novel predictive neural network that deterministically and uniformly models the transient variations and cumulative trends embedded in cloud image sequences under the supervision of an attention mechanism to characterize complex spatiotemporal dynamics. The context-gated unit (CGU) is connected to the encoder and decoder, replenishing context features lost by downsampling while removing the “ghosting” effect prevalent in spatiotemporal prediction tasks. For the purpose of evaluation of the proposed method, a series of comparative experiments and ablation studies are conducted on our collected TSI-440-Sequence Dataset (TSISD) dataset. Experimental results indicate that the proposed method outperforms other existing methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • ATASI-Net: An Efficient Sparse Reconstruction Network for Tomographic SAR
           Imaging With Adaptive Threshold

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      Authors: Muhan Wang;Zhe Zhang;Xiaolan Qiu;Silin Gao;Yue Wang;
      Pages: 1 - 18
      Abstract: Tomographic synthetic aperture radar (SAR) technique has attracted remarkable interest for its ability of 3-D resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into SAR tomography (TomoSAR) considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this article proposes a novel and efficient sparse unfolding network based on the analytic learned iterative shrinkage-thresholding algorithm (ALISTA) architecture with adaptive threshold, named adaptive threshold ALISTA-based sparse imaging network (ATASI-Net). The weight matrix in each layer of ATASI-Net is precalculated as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. Furthermore, the introduction of an adaptive threshold for each azimuth–range pixel permits the threshold shrinkage to be not only layer-varied but also elementwise. In addition, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Fine Aligned Discriminative Hashing for Remote Sensing Image-Audio
           Retrieval

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      Authors: Yaxiong Chen;Jinghao Huang;Shengwu Xiong;Xiaoqiang Lu;
      Pages: 1 - 12
      Abstract: For cross-modal remote sensing image-audio (RSIA) retrieval task, hashing technology has attracted much attention in recent works. Most of them focus on mapping RS images and audios into a Hamming space, whilst neglecting discriminative information of RS images and fine alignment for RS images and audios. In this article, we tackle these dilemmas with a novel fine aligned discriminative hashing (FADH) approach, which can learn hash codes to capture discriminative information of RS images and learn the corresponding detailed information between RS images and audios simultaneously. We first develop a new discriminative information learning module to learn discriminative information about RS images. Meanwhile, a fine alignment module is proposed to unearth the fine correspondence for RS image regions and audios, which can effectively improve the retrieval performance. On top of the two paths, we design a new objective function, which can maintain the similarity of hash codes, preserve the semantic information of RS image features and audio features and eliminate cross-modal differences. The reliability and significance of the designed framework are effectively demonstrated by diverse experiments on three RSIA datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Estimating the Angular Distribution of the Earth’s Longwave
           Radiation From Radiative Fluxes

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      Authors: Huizeng Liu;Qingquan Li;Shaopeng Huang;Hong Qiu;Huiping Jiang;Chao Yang;Ping Zhu;
      Pages: 1 - 11
      Abstract: In recent years, several novel satellite platforms and sensors have been proposed for the Earth radiation budget (ERB). Simulating the sensor-measured signals could be helpful for optimizing the settings of sensors and exploring their potential in ERB. The anisotropic factor, depicting the anisotropy of Earth’s radiation, is essential in the simulation. However, developing angular distribution models (ADMs) involves complex procedures of data preparation, processing, and modeling. This study, targeting at simplifying the procedure of simulating the signals of ERB sensors, proposed a suit of models for estimating the longwave anisotropic factors directly from the Earth’s radiative fluxes. The models were developed with CERES/Terra data sensed in rotating azimuth plane (RAP) mode during 2000–2005 and the artificial neural network (ANN) algorithm and tested with 12 monthly of CERES/Terra data collected in RAP and cross-track (CT) mode during 2021–2022, respectively. Models were developed for ten scene types based on Earth’s surface types and compared with the operational ANN ADMs. Results showed that the longwave anisotropic factors were accurately estimated with the correlation coefficient ( $r$ ) varying between 0.84 and 0.98 and mean absolute percentage error (MAPE) within 1.20% for the test dataset, and the approach proposed in this study had comparable performance with the ANN ADMs. With the estimated anisotropic factors, the sensor-measured radiances were accurately retrieved with $r$ = 1.00 and MAPE = 0.53%. Therefore, the proposed approach is promising in accurate and efficient simulations of novel ERB platforms and sensors like the Moon-based Earth R-diation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Continuous Remote Sensing Image Super-Resolution Based on Context
           Interaction in Implicit Function Space

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      Authors: Keyan Chen;Wenyuan Li;Sen Lei;Jianqi Chen;Xiaolong Jiang;Zhengxia Zou;Zhenwei Shi;
      Pages: 1 - 16
      Abstract: Despite its fruitful applications in remote sensing, image super-resolution (SR) is troublesome to train and deploy as it handles different resolution magnifications with separate models. Accordingly, we propose a highly applicable SR framework called FunSR, which settles different magnifications with a unified model by exploiting context interaction within implicit function space. FunSR composes a functional representor, a functional interactor, and a functional parser. Specifically, the representor transforms the low-resolution image from Euclidean space to multiscale pixelwise function maps; the interactor enables pixelwise function expression with global dependencies; and the parser, which is parameterized by the interactor’s output, converts the discrete coordinates with additional attributes to RGB values. Extensive experimental results demonstrate that FunSR reports the state-of-the-art performance on both fixed- and continuous-magnification settings; meanwhile, it provides many friendly applications due to its unified nature. Our code is available at https://github.com/KyanChen/FunSR.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Methodology to Assess Spatial Response Models for Satellite Imagers
           Using the Optical Design Parameters of a Generic Sensor as Independent
           Variables

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      Authors: Alvaro Q. Valenzuela;Karin Reinke;Simon D. Jones;
      Pages: 1 - 10
      Abstract: Several types of analytic models are currently used to estimate the spatial response of satellite imagers, the accuracy of these models being critical for applications requiring precise knowledge about the spatial response of a given imager. The assessment of these models is complicated because the actual spatial response of an imager depends on its optical design, so evaluations based on a single kind of design are inherently biased. To reduce this bias, a new assessment methodology based on a generic imaging sensor is proposed; the key optical design parameters of this sensor are selected as independent variables, so the error of any spatial response model can be computed within a broad domain of possible optical designs. Assuming a generic sensor with an annular optical aperture and square detector elements, the optical factor $Q$ and the aperture obstruction ratio $varepsilon $ are selected as key design parameters, allowing the error of spatial response models to be computed in the ( $Q$ , $varepsilon$ ) plane. This approach is used to assess the separable point spread function (PSF) model, which assumes that the PSF is equal to the product of two perpendicular line spread functions (LFSs), concluding that it is only valid when $Q le0.35$ for PSF $ge0.1$ . This methodology can be used to assess other types of spatial response models for different shapes of optical apertures and detect elements. We contend that our approach provides a standard assessment procedure that will help end users select the correct model for th-ir specific application.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Dual-Field-of-View Context Aggregation and Boundary Perception for Airport
           Runway Extraction

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      Authors: Wei Jing;Yuan Yuan;Qi Wang;
      Pages: 1 - 12
      Abstract: Airport runway extraction is important for daily maintenance of civil airports, precision strikes at military airports, and safe landing of UAVs. However, different from the typical object extraction tasks, objects such as runways, taxiways, and roads share extremely similar attributes in terms of material, texture, and shape, making it challenging to differentiate between them. Besides, the gradients of some runway boundaries change slowly and are difficult to extract accurately. To address these problems, a dual-field-of-view context and boundary perception network (DCBP) is proposed, which can combine long–short-term contexts and boundary information of runways. Specifically, the dual-field-of-view context aggregation (DCA) module can discover semantic representations from two perspectives by exploring the interaction between long-term and short-term contexts. Meanwhile, the detailed features learned from the high-resolution branch are used to guide the boundary perception (BP) module in learning the location of the runway boundaries. In addition, we provide the research community with a precisely labeled dataset, named the airport runway segmentation (ARS) dataset, to advance runway segmentation with remote sensing images. Extensive experiments on the benchmark demonstrated that DCBP achieved more accurate extraction results and obtained sharper boundaries on various airport runways than other methods. The code and dataset are available at https://github.com/weiAI1996/DCBP.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Utilizing Bounding Box Annotations for Weakly Supervised Building
           Extraction From Remote-Sensing Images

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      Authors: Daoyuan Zheng;Shengwen Li;Fang Fang;Jiahui Zhang;Yuting Feng;Bo Wan;Yuanyuan Liu;
      Pages: 1 - 17
      Abstract: Image-level weakly supervised semantic segmentation (WSSS) methods have greatly facilitated the extraction of buildings from remote-sensing (RS) images. However, the lack of the locations and extent of individual buildings in image-level labels results in some limitations of the methods, especially in the cases of cluttered backgrounds and diverse building shapes and sizes. By utilizing bounding box annotations, a novel WSSS model is developed to improve building extraction from RS images in this article. Specifically, during the training phase, a multiscale feature retrieval (MFR) module is designed to learn multiscale building features and suppress the background noise inside the bounding box. In the inference phase, multiscale class activation maps (CAMs) are generated from multiscale features to achieve accurate building localization. Finally, a pseudo-mask generation and correction (PGC) module refines the CAMs to generate and correct the building pseudo-masks. Experiments are conducted to examine the proposed model in three datasets, namely the WHU aerial building dataset, the CrowdAI building dataset, and a self-annotated building dataset. Experimental results demonstrate that the proposed method outperforms baselines, achieving 76.99%, 75.51%, and 67.35% in terms of intersection over union (IoU) scores on the three challenging datasets, respectively. This article provides a methodological reference for the application of weakly supervised learning on RS images.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Remote Sensing Image Ship Detection Based on Dynamic Adjusting Labels
           Strategy

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      Authors: Chaofan Pan;Runsheng Li;Wei Liu;Wanjie Lu;Chaoyang Niu;Quanfu Bao;
      Pages: 1 - 21
      Abstract: Remote sensing ship detection is a hotspot in computer vision, which is vital in both military and civilian fields. Nevertheless, the arbitrary orientation and dense arrangement of ship targets impose significant challenges in high-precision detection. Although research on this problem has progressed, high-precision detection is still limited by angular prediction accuracy. To tackle this issue, we start with angle prediction and propose a dynamic adjusting labels (DAL) strategy based on binary coded label (BCL). DAL strategy dynamically adjusts the ground-truth coded labels in the training process to guide angle coding for tendency learning. This strengthens the coupling between the angle coding bits and improves the performance of small granularity intervals. Due to the angle interval granularity difference, the learning difficulty of the coding layers and the convergence speed vary significantly. Aiming at this problem, we add a gradient truncation mechanism to each coding bit loss. The mechanism can effectively balance the coding layers’ learning strength and enhance the model’s training emphasis on coding bits corresponding to small granularity intervals, thus avoiding the effect of coding bits learning imbalance on angle prediction. Extensive experiments based on three public datasets demonstrate our method’s superiority in high-precision detection and the state-of-the-art performance. Our code is available at https://github.com/lirunsheng2008/SDALS.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Evaluation of Three Land Surface Temperature Products From Landsat Series
           Using in Situ Measurements

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      Authors: Mengmeng Wang;Can He;Zhengjia Zhang;Tian Hu;Si-Bo Duan;Kaniska Mallick;Hua Li;Xiuguo Liu;
      Pages: 1 - 19
      Abstract: Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST- which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • KCPNet: Knowledge-Driven Context Perception Networks for Ship Detection in
           Infrared Imagery

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      Authors: Yaqi Han;Jingwen Liao;Tianshu Lu;Tian Pu;Zhenming Peng;
      Pages: 1 - 19
      Abstract: Ship detection plays a crucial role in a variety of military and civilian marine inspection applications. Infrared images are irreplaceable data sources for ship detection due to their strong adaptability and excellent all-weather reconnaissance ability. However, previous researches mainly focus on visible light or synthetic aperture radar (SAR) ship detection, while infrared ship detection is left in a huge blind spot. The main obstacles to this dilemma lie in the absence of public datasets, small scale, and poor semantic information of infrared ships, and severe clutter in complex ocean environments. To address the above challenges, we propose a knowledge-driven context perception network (KCPNet) and construct a public dataset called infrared ship detection dataset (ISDD). In KCPNet, aiming at the small scale of infrared ships, a balanced feature fusion network (BFF-Net) is proposed to balance information from all backbone layers and generate nonlocal features with balanced receptive fields. Moreover, considering the key role of contextual information, a contextual attention network (CA-Net) is designed to improve robustness in complex scenes by enhancing target and contextual information and suppressing clutter. Inspired by prior knowledge of human cognitive processes, we construct a novel knowledge-driven prediction head to autonomously learn visual features and back-propagate the knowledge throughout the whole network, which can efficiently reduce false alarms. Extensive experiments demonstrate that the proposed KCPNet achieves state-of-the-art performance on ISDD. Source codes and ISDD are accessible at https://github.com/yaqihan-9898.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Thermal UAV Image Super-Resolution Guided by Multiple Visible Cues

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      Authors: Zhicheng Zhao;Yong Zhang;Chenglong Li;Yun Xiao;Jin Tang;
      Pages: 1 - 14
      Abstract: Unmanned aerial vehicle (UAV) thermal-imaging has received much attention, but the insufficient image resolution caused by thermal imaging systems is still a crucial problem that limits the understanding of thermal UAV images. However, high-resolution visible images are relatively easy to access, and it is thus valuable for exploring useful information from visible image to assist thermal UAV image super-resolution (SR). In this article, we propose a novel multiconditioned guidance network (MGNet) to effectively mine the information of visible images for thermal UAV image SR. High-resolution visible UAV images usually contain salient appearance, semantic, and edge information, which plays a critical role in boosting the performance of thermal UAV image SR. Therefore, we design an effective multicue guidance module (MGM) to leverage the appearance, edge, and semantic cues from visible images to guide thermal UAV image SR. In addition, we build the first benchmark dataset for the task of thermal UAV image SR guided by visible images. It is collected by a multimodal UAV platform and composes of 1025 pairs of manually aligned visible and thermal images. Extensive experiments on the built dataset show that our MGNet can effectively leverage useful information from visible images to improve the performance of thermal UAV image SR and perform well against several state-of-the-art methods. The dataset is available at: https://github.com/mmic-lcl/Datasets-and-benchmark-code.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Sparse Regularization-Based Spatial–Temporal Twist Tensor Model for
           Infrared Small Target Detection

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      Authors: Jie Li;Ping Zhang;Lingyi Zhang;Zhiyuan Zhang;
      Pages: 1 - 17
      Abstract: Infrared (IR) small target detection under complex environments is an essential part of IR search and track systems. However, previously proposed IR small target detection algorithms cannot achieve complete suppression of complex and significant backgrounds. The spatial–temporal information of image sequences is not fully exploited. In this article, we present a sparse regularization-based twist tensor model for IR small target detection. First, the twist tensor model is built via perspective conversion based on the target’s local continuity in the spatial–temporal domain, which makes the original complicated background components more structured and increases the difference between the background and the target. Then, the structured sparsity-inducing norm is introduced to define the locality and continuity of the target. To further minimize the sparse background structures and global noise, the structured sparsity-inducing norm and the $l_{1}$ norm are combined as the target’s parse constraint. Experimental results on real scenes reveal that the suggested method can process images with high detection accuracy and outstanding background suppression ability compared to various state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Receptive-Field and Direction Induced Attention Network for Infrared Dim
           Small Target Detection With a Large-Scale Dataset IRDST

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      Authors: Heng Sun;Junxiang Bai;Fan Yang;Xiangzhi Bai;
      Pages: 1 - 13
      Abstract: Infrared small target detection plays an important role in military and civilian fields while it is difficult to be solved by deep learning (DL) technologies due to scarcity of data and strong interclass imbalance. To relieve scarcity of data, we build a massive dataset IRDST, which contains 142 727 frames. Also, we propose a receptive-field and direction-induced attention network (RDIAN), which is designed using the characteristics of target size and grayscale to solve the interclass imbalance between targets and background. Using convolutional layers with different receptive fields in feature extraction, target features in different local regions are captured, which enhances the diversity of target features. Using multidirection guided attention mechanism, targets are enhanced in low-level feature maps. Experimental results with comparison methods and ablation study demonstrate effective detection performance of our model. Dataset and code will be available at https://xzbai.buaa.edu.cn/datasets.html.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Optimizing the Protocol of Near-Surface Remote Sensing Experiments Over
           Heterogeneous Canopy Using DART Simulated Images

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      Authors: Biao Cao;Jean-Philippe Gastellu-Etchegorry;Tiangang Yin;Zunjian Bian;Junhua Bai;Junyong Fang;Boxiong Qin;Yongming Du;Hua Li;Qing Xiao;Qinhuo Liu;
      Pages: 1 - 16
      Abstract: Optical canopy models that connect land surface properties and satellite-observed radiance must be validated before being used. These models include the bidirectional reflectance distribution function (BRDF) models in the visible and near-infrared domains, and directional brightness temperature (DBT) models in the thermal infrared domain. Near-surface experiments have been extensively conducted to evaluate the modeling accuracy, including ground-, tower-, and aircraft-based measurements. Indeed, it should be noted that in situ measured BRDF/DBT results are sensitive to the experiment protocol, such as sensor moving orientation, flight height, and sampling frequency. A practical tool for optimizing the in situ measurement protocols is needed in the community of remote sensing modeling. For that, we devised a virtual experiment framework based on the discrete anisotropic radiative transfer (DART) 3-D radiative transfer model that is capable of simultaneously simulating both the BRDF/DBT pattern and the images acquired by in situ cameras. Here, as an optimization case, we use it to determine the optimal sensor flight orientation over heterogeneous vegetated canopies (a row-planted scene with three solar angles and a discrete scene with three solar angles) for measuring their DBT distribution. Results showed considerable errors (i.e., image-extracted DBT minus DART-simulated DBT) exist for sensor flight orientation along the canopy rows ( $R^{2}$ = 0.24 and root mean square error (RMSE) = 4.32 K), and they become much smaller ( $R^{2}$ = 0.94 ~ 0.98 and RMSE = 0.82 ~ 1.03 K) in other typical orientations (e.g., cross row plane, solar principal plane, and cross solar principal plane). The critical azimuth offset rela-ive to the row direction that can ensure an acceptable RMSE < 1 K is quantified as atan(3*Unitwidth/Scenesize) based on a series of intensive simulations by this new tool. However, the RMSE of the discrete scene is not sensitive to the flight orientation. Such accuracy differences in various protocols were experimentally verified over row-planted maize using a 4-D tower in Huailai, Hebei, China. The result highlights the great potential of this newly designed DART-based virtual experiment to optimize near-surface experiment protocols.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Detection of Subtle Thermal Anomalies: Deep Learning Applied to the ASTER
           Global Volcano Dataset

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      Authors: Claudia Corradino;Michael S. Ramsey;Sophie Pailot-Bonnétat;Andrew J. L. Harris;Ciro Del Negro;
      Pages: 1 - 15
      Abstract: Twenty-one years of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a convolutional neural network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely, Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global scale using future TIR data such as the planned NASA Surface Biology and Geology (SBG) mission.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Infrared Small Target Detection Based on Local Contrast-Weighted
           Multidirectional Derivative

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      Authors: Yunkai Xu;Minjie Wan;Xiaojie Zhang;Jian Wu;Yili Chen;Qian Chen;Guohua Gu;
      Pages: 1 - 16
      Abstract: Realizing robust infrared small target detection in complex backgrounds is of great essence for infrared search and tracking (IRST) applications. However, the high-intensity structures in background regions, such as the sharp edges, make it a challenging task, especially when the target is with low signal-to-clutter ratio (SCR). To address this issue, we propose an infrared small target detection method using local contrast-weighted multidirectional derivative (LCWMD). It is a robust detector that comprehensively considers the target property, background information, and the relation between them. First, we consider the approximate isotropy of the infrared small target and present a new multidirectional derivative with penalty factors based on the Facet model to develop the target salience in the local region. Second, a dual local contrast fusion model with the trilayer design is introduced to amplify the difference between the target and the background, so as to further suppress the high-intensity structural clutters. Finally, the LCWMD map is obtained by weighting the above two filtered maps, after which an adaptive segmentation operation is applied to accomplish the target detection. The results of comparative experiments implemented on real infrared images demonstrate that our method outperforms other state-of-the-art detectors by several times in terms of SCR gain (SCRG) and background suppression factor (BSF).
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • One-Stage Cascade Refinement Networks for Infrared Small Target Detection

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      Authors: Yimian Dai;Xiang Li;Fei Zhou;Yulei Qian;Yaohong Chen;Jian Yang;
      Pages: 1 - 17
      Abstract: Single-frame infrared small target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this article, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudobox-based label assignment scheme that relaxes the constraints on the scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposal for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model- and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Physical-Based Method for Pixel-by-Pixel Quantifying Uncertainty of Land
           Surface Temperature Retrieval From Satellite Thermal Infrared Data Using
           the Generalized Split-Window Algorithm

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      Authors: Yang Gui;Si-Bo Duan;Zhao-Liang Li;Cheng Huang;Meng Liu;Xiangyang Liu;Caixia Gao;
      Pages: 1 - 15
      Abstract: Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared (TIR) data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to the simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’s field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can characterize the spatial variation in LST uncertainties well over various land cover types.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Chang’E-4 Measurements of Lunar Surface Temperatures: Thermal
           Conductivity of the Near Surface Regolith

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      Authors: Wenchao Zheng;Guoping Hu;Yunzhao Wu;Qi Jin;Zhengmei Li;Li Feng;
      Pages: 1 - 14
      Abstract: Lunar thermal conductivity is significantly important for understanding the geological processes of the Moon. The Apollo in situ heat flow experiments and diviner remote sensing data provide us with good constraints on the thermophysical properties of the lunar regolith fines layer. As the first farside in situ experiments, Chang’E-4 (CE-4) temperature sensors will further extend our understanding. In this study, a 1-D thermal model with updated bulk densities by the CE-4 lunar penetrating radar (LPR) data is used. Then, the nighttime surface temperatures from the sensors of the CE-4 lander are applied to retrieve the surface thermal conductivity at the CE-4 landing site. After minimizing the differences between the predicted temperature and CE-4 measurements, the range of surface layer contact conductivity ( $k_{s}$ ) within the upper 1 cm is about (0.95–2.26) $times 10^{-3},,text {Wm}^{-1}cdot text {K}^{-1}$ for T1, (1.04–2.44) $times 10^{-3},,text {Wm}^{-1}cdot text {K}^{-1}$ for T2, (0.60–1.40) $times 10^{-3},,text {Wm}^{-1}cdot text {K}^{-1}$ for T3, and (0.60–1.39) $times 10^{-3},,text {Wm}^{-1}cdot text {K}^{-1}$ for T4, respectively. In addition, the factors that may affect the inversion or the measurements were discussed, such as the shadowing effect, the rock abundance (RA), the lateral conduction from the rover transfer mechanism, and the densification effect during the measurements.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Temporal Upscaling of MODIS 1-km Instantaneous Land Surface Temperature to
           Monthly Mean Value: Method Evaluation and Product Generation

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      Authors: Xiangyang Liu;Zhao-Liang Li;Jia-Hao Li;Pei Leng;Meng Liu;Maofang Gao;
      Pages: 1 - 14
      Abstract: The monthly mean land surface temperature (MMLST) reflects more stable intra- and interannual temperature variations, and therefore, it has a wider range of applications than instantaneous land surface temperature (LST). This study aimed to generate a high-resolution global MMLST product by temporally upscaling the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km instantaneous LST. First, six current methods were comprehensively evaluated using cross-validation technology. These six methods are the cross combinations of two temporal aggregation schemes: the average by observations (ABO) and average by days (ABD), and three conversion models: the diurnal temperature cycle model (DTC), the simple average of two instantaneous LSTs (TSA), and a weighted average model for multiple instantaneous LSTs (MWA). The analysis with measurements from 235 flux stations worldwide revealed that the choice of conversion model considerably affected the overall retrieval accuracy, whereas the influence of the aggregation scheme was minor. From the conversion model standpoint, MWA performed best, followed by DTC, and finally TSA; this order remained the same even if DTC and TSA were improved with mean bias correction. Notably, the errors of ABDMWA decreased as the number of daily mean LST (NOD) increased, whereas the errors of ABOMWA were not related to NOD. Accordingly, we deduced that the optimal strategy for estimating MMLST is using ABOMWA when NOD is < 20 and ABDMWA when NOD is $ge 20$ . Subsequently, we adopted this combination method to process MODIS instantaneous LSTs and produced a global 1-km MMLST dataset for the years 2003–2020. The validation showed a satisfactory accuracy with a root mean square error (RMSE) of 1.6 K. The intercomparison with MMLSTs from geostationary (GEO) satellites (containing complete LST daily cycle) presented a good ag-eement (biases < 0.3 K and STDs < 2 K). Compared with atmospheric infrared sounder (AIRS) L3 monthly standard physical retrieval (AIRS3STM) product which had the same temporal span, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature. Most importantly, it had a prominently better ability to retrieve spatial details of temperature variations due to its higher resolution. Our new method and product show promising prospects for applications in global change studies, where accurate spatially resolved MMLST data are one of the fundamental geophysical variables required.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Two-Stage Destriping Algorithm Based on MWIR Energy Separation and
           Image Guidance (MES-IG)

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      Authors: Xinyu Zhou;Ye Zhang;Jinhao Liu;Yue Hu;
      Pages: 1 - 16
      Abstract: Long-wave infrared (LWIR) bands in multispectral datasets are extremely useful in many applications. However, the LWIR bands usually suffer from undesirable stripe noise, which impedes their further application. Compared with emission-dominated LWIR, the mid-wave infrared (MWIR) bands containing both emitted and reflected radiation usually exhibit higher image quality. In this article, we propose a novel two-stage MWIR energy separation and image guidance (MES-IG) algorithm to destripe the LWIR images with the assistance of the MWIR bands. In the first stage, we decompose the MWIR image into the emitted and reflected components by solving a constrained optimization problem. Specifically, we impose the low-rank penalty to enforce the similarities between MWIR and LWIR, and we use the total variation (TV) regularization to exploit the similarities between MWIR and visible and near-infrared (VNIR) images. In the second stage, the obtained emitted component of MWIR is considered as the guidance image to remove the stripes in the LWIR images by adopting the 1-D guided filter algorithm. Numerical experiments on the Chinese Gaofen-5 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate the utility of the proposed method in providing improved LWIR image destriping performance over the state-of-the-art algorithms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Analytical Study of the Changes in Brightness Temperature Based on the
           Tectonic Field Associated With Three Earthquakes in the Eastern Tibetan
           Plateau

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      Authors: Xing Yang;Qian Lu;Tiebao Zhang;Fang Du;Feng Long;Min Zhao;Xiaofeng Liao;
      Pages: 1 - 15
      Abstract: The thermal infrared brightness temperature (BT) of the eastern Tibetan Plateau (TP) was retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) level-1B data. The multiyear averaged BT background field was subtracted from the punctual BT data to yield monthly BT spatial anomaly, and calculated time series of BT for the secondary blocks. Then, the spatial and temporal changes in the BT of the study area before the Menyuan M6.4, Zaduo M6.2, and Jiuzhaigou M7.0 earthquakes were investigated and analyzed based on the tectonic setting. The results show the following. The spatial BT radiation enhancement frequency rose remarkably before strong earthquakes; each of the three earthquakes was preceded by marked spatiotemporal continuous BT anomalies. The tectonic setting significantly influences the BT anomaly feature. The spatial BT anomaly was not notable in the Qaidam and Qilian block before the Menyuan earthquake; the spatial BT anomaly mainly appeared in the Qiangtang and Bayan Har blocks before the Zaduo and Jiuzhaigou earthquakes. The Qiangtang and Bayan Har block’s BT time series curves have similar features. The Qaidam and Qilian block’s BT time series curves have analogous shapes. The three earthquakes may be regarded as one seismic event induced by a stage of tectonic stress enhancement rather than three independent occasions. The spatial BT anomalous behavior before earthquakes is, to a great extent, like the result of the rock stress loading experiment; the rock compression and the lithosphere–atmosphere–ionosphere coupling (LAIC) may be the main reasons for the intensification of the BT radiation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Estimating Hourly All-Weather Land Surface Temperature From FY-4A/AGRI
           Imagery Using the Surface Energy Balance Theory

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      Authors: Weihan Liu;Jie Cheng;Qiao Wang;
      Pages: 1 - 18
      Abstract: Thermal infrared (TIR) observations from geostationary satellites enable the retrieval of the diurnal variations in land surface temperatures (LSTs) linked to the land–atmosphere energy exchange and the water cycle. However, cloud cover obstructs TIR signals and leads to missing gaps that generally account for more than 50% of TIR LST maps. This study proposed an effective method to estimate hourly all-weather LST from the Advanced Geosynchronous Radiation Imager (AGRI) data under the framework of surface energy balance (SEB) theory. An improved temperature and emissivity separation algorithm was first used to obtain the high-quality clear-sky LST, which plays a decisive role in ensuring the accuracy of recovered cloudy-sky LST. Then, we proposed a unique way to solve the temperature difference ( $Delta $ LST) between the cloudy-sky LST and hypothetical clear-sky LST caused by cloud radiative effects (CREs). The bias and root mean squared error (RMSE) of the estimated AGRI hourly cloudy-sky LST are 0.10 K and 3.71 K during the daytime, and −0.20 K and 2.73 K during the nighttime, respectively. The overall bias (RMSE) of the estimated AGRI all-weather LST is 0.02 K (2.84 K). The estimated hourly all-weather LST not only captures the rapid variation in diurnal LST but is also promising for temporal upscaling. The temporally upscaled daily mean LST shows a bias (RMSE) of 0.03 K (1.35 K). This study provides a promising solution to generate diurnal hourly all-weather LST for AGRI and other geostationary satellites.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An Integrated Method for the Generation of Spatio-Temporally Continuous
           LST Product With MODIS/Terra Observations

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      Authors: Yao Xiao;Wei Zhao;Mingguo Ma;Wenping Yu;Lei Fan;Yajun Huang;Xupeng Sun;Qing Lang;
      Pages: 1 - 14
      Abstract: Land surface temperature (LST) is a crucial parameter in the study of land surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and the China land data assimilation system (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information, and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Dim2Clear Network for Infrared Small Target Detection

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      Authors: Mingjin Zhang;Rui Zhang;Jing Zhang;Jie Guo;Yunsong Li;Xinbo Gao;
      Pages: 1 - 14
      Abstract: Infrared small target detection (IRSTD) is important for many practical applications such as hazardous aircraft warning, especially when the target is not visible in visible light image due to atmospheric conditions such as fog and cloud. However, IRSTD is challenging due to noises, small and dim targets. To address this challenge, we propose a novel Dim2Clear network (Dim2Clear) for IRSTD in this article. Specifically, the Dim2Clear consists of a U-Net backbone encoder, a context mixer decoder (CMD) based on spatial and frequency attention (SFA), and an eyeball-shaped enhancement module (EEM). The CMD is composed of cascaded regular residual blocks where two SFA modules are inserted. Each SFA module receives features from different residual blocks and generates spatial attention map from them to modulate the low-level features, which are then decomposed into low and high frequencies using the discrete cosine transformation. Accordingly, features are further modulated according to the generated frequency attention maps. In this way, SFA can extract both spatial context and frequency context to improve the feature representation capacity. In addition, we design an EEM to suppress the noise and enhance the signal-to-noise ratio (SNR) in the segmentation results from the perspective of image super-resolution. Experiments on the SIRST dataset and our newly constructed IRSTD-1k dataset show that the proposed Dim2Clear outperforms the state-of-the-art (SOTA) methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for
           

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      Authors: Shugui Zhou;Jie Cheng;
      Pages: 1 - 25
      Abstract: Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and the atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from 27 sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and root mean square error (RMSE) were 1.65 and 16.23 W/ $text{m}^{2}$ for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 and 18.51 W/ $text{m}^{2}$ , and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were −1.64 and 17.44 W/ $text{m}^{2}$ , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water (TPW) vapor was less than 1.5 cm. However, under a large TPW (i.e., TPW $>$ 3 cm), especially in the case of a large viewing zenith angle (i.e., VZA $> 45^{circ }$ ), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction, and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/ $text{m}^{2}$ . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiinformation Fusion Network for Mapping Gapless All-Sky Land Surface
           Temperature Using Thermal Infrared and Reanalysis Data

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      Authors: Yong Zhang;Yingbao Yang;Xin Pan;Yuan Ding;Jia Hu;Yang Dai;
      Pages: 1 - 15
      Abstract: Toward fully exploring multidisciplinary research topics under global warming, the spatiotemporal discontinuities of land surface temperature (LST) products due to cloud contamination still challenge such research topics. Data fusion (DF) that seeks the best compromise between multiple data sources plays a key role in providing gapless LST data. However, most models only use information about the variation of LST at different times or the difference between different LST products without effectively combining the two pieces of information. With the rapid development of deep-learning methods, powerful modeling capabilities can solve this problem. This article proposes a novel multiinformation fusion network (MIFN) based on convolutional neural networks (CNNs) and attention mechanisms (AMs) to map gapless all-sky LST, taking temporal-changing (TC) and data-differentiated (DD) information into consideration. Temporal normalization (TN) is used as a preprocessing step to match the time of moderate resolution imaging spectroradiometer (MODIS) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis Fifth-Generation (ERA5) LSTs. The MIFN extracts multiscale multitemporal TC and DD features by network constraints. Then, the weights of the target LST features reconstructed by TC and DD features are assigned through an AM to fuse them reasonably. Finally, we design a loss function that combines TC, DD, and LST reconstruction to improve the accuracy of LST prediction further. We performed comprehensive data experiments to validate the performance of the new method. Our technique is more advantageous in generating MODIS-like LSTs than four state-of-the-art methods and two CNN models using a single piece of information.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Temperature and Emissivity Retrieval From Hyperspectral Thermal Infrared
           Data Using Dictionary-Based Sparse Representation for Emissivity

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      Authors: Chenyang Ma;Yonggang Qian;Kun Li;Xianhui Dou;Huanfeng Shen;Hongzhao Tang;Shi Qiu;Lihua Zhang;Yuanyuan Jia;Guangzhou Ou-Yang;
      Pages: 1 - 16
      Abstract: The separation of land surface temperature (LST) and land surface emissivity (LSE) is an ill-posed problem in thermal infrared (TIR) remote sensing. By building a new observation matrix to compress the LSE unknown and a dictionary training method to reconstruct complete LSE spectra, a new dictionary-based sparse representation for emissivity (DSRE) method has been proposed to retrieve LST and LSE from the atmospherically corrected hyperspectral TIR data. The proposed method fully utilizes the sparsity of compressed sensing and the empirical knowledge of the trained emissivity dictionary. The sensitivity analysis shows that the modeling accuracies of the proposed method are 0.215 K and 0.0060 for LST and LSE, respectively. Even with the instrument noise of 0.3 K and the uncertainties in atmospheric transmittance, atmospheric upwelling, and downwelling radiance of 10%, the retrieval accuracies are 0.811 K for LST and 0.0241 for LSE, respectively. Then a field experiment was conducted to validate the proposed method, and a comparison was executed to three published methods, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature-emissivity separation (TES) (ASTERTES), linear spectral emissivity constraint TES (LSECTES), and iterative spectrally smooth TES (ISSTES). The accuracies of retrieved LST and spectral LSE are 1.41 K/0.009, 2.57 K/0.071, 1.59 K/0.038, and 2.00 K/0.077 for DSRE, ASTERTES, LSECTES, and ISSTES. In contrast to the three published methods, our proposed method is more accurate and effective than other published methods. Especially in the atmospheric absorption band, the proposed method has a strong anti-noise capability to the residuals of environmental downwelling radiance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiscale Multilevel Residual Feature Fusion for Real-Time Infrared Small
           Target Detection

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      Authors: Hai Xu;Sheng Zhong;Tianxu Zhang;Xu Zou;
      Pages: 1 - 16
      Abstract: Detecting infrared dim and small targets is one crucial step for many tasks such as early warning. It remains a continuing challenge since characteristics of infrared small targets, usually represented by only a few pixels, are generally not salient. Even though many traditional methods have significantly advanced the community, their robustness or efficiency is still lacking. Most recently, convolutional neural network (CNN)-based object detection has achieved remarkable performance and some researchers focus on it. However, these methods are not computationally efficient when implemented on some CPU-only machines and a few datasets are available publicly. To promote the detection of infrared small targets in complex backgrounds, we propose a new lightweight CNN-based architecture. The network contains three modules: the feature extraction module is designed for representing multiscale and multilevel features, the grid resample operation module is proposed to fuse features from all scales, and a decoupled head to distinguish infrared small targets from backgrounds. Moreover, we collect a brand-new infrared small target detection dedicated dataset, which consists of 68311 practical captured images with complex backgrounds for alleviating the data dilemma. To validate the proposed model, 54758 images are used for training and 13553 images are used for testing. Extensive experimental results demonstrate that the proposed method outperforms all traditional methods by a large margin and runs much faster than other CNN methods with high precision. The proposed model can be implemented on the Intel i7-10850H CPU (2.3 GHz) platform and Jetson Nano for real-time infrared small target detection at 44 and 27 FPS, respectively. It can be even deployed on an Atom x5-Z8500 (1.44 GHz) machine at about 25 FPS with $128times128$ local images. The source codes and the dataset have been made publicly available-at https://github.com/SeaHifly/Infrared-Small-Target.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Thermal Infrared Radiative Transfer Modeling in Urban Areas by Considering
           3-D Structures and Sunlit–Shadow Temperature Contrast

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      Authors: Xiaopo Zheng;Tianxing Wang;Francoise Nerry;Youying Guo;Zhihao Huang;
      Pages: 1 - 17
      Abstract: Land surface temperature (LST) is a crucial parameter needed to study the thermal environment in urban areas. Currently, it can be restored from thermal infrared (TIR) measurements based on various LST retrieval algorithms. However, the expected urban LST retrieval accuracy of < 1 K is difficult to achieve because knowledge is lacking on how to correct the impact from the surface of 3-D structures and the sunlit–shadow temperature contrast. Although an analytical TIR radiative transfer model over urban area (ATIMOU) has been proposed, the temperature contrast between sunlit and shadowed areas has not been well-managed yet, thus leading to its inapplicability in daytime TIR observations. This study develops an extended ATIMOU (E_ATIMOU) that considers the impact from both 3-D structures and sunlit–shadow temperature contrast. According to the simulations based on E_ATIMOU, if such impact is not properly accounted for, a 4.43-K bias can be potentially introduced to the ground brightness temperature of a street canyon under the condition of wavelength of $10~mu text{m}$ , ratio “sunlit-road area/total-road area” of 0.5, shadowed wall and road temperature of 300 K, and the sunlit–shadow temperature contrast of 5 K, which emphasizes the necessity of addressing this impact during the LST retrieval in urban areas. Moreover, E_ATIMOU has also been validated by intercomparing with the discrete anisotropic radiative transfer (DART) model. The discrepancy between the two models for the calculated ground brightness temperatures is found to be < 0.1 K for various urban scenarios, indicating that the E_ATIMOU is in good agreement with DART.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Oriented Infrared Vehicle Detection in Aerial Images via Mining Frequency
           and Semantic Information

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      Authors: Nan Zhang;Youmeng Liu;Hao Liu;Tian Tian;Jinwen Tian;
      Pages: 1 - 15
      Abstract: Infrared vehicle detection based on aerial images has significant applications in military and civilian fields for the perception ability under low-light and foggy conditions. However, it remains challenging due to the following characteristics. First, infrared textures and edges are blurred, implying a plenty of low-frequency signals and a shortage of detailed descriptions. Second, objects in infrared images present different patterns depending on their thermal radiations, which hamper the feature extraction of convolution kernels. Third, infrared images lack color information, which means that fewer features for classification and regression can be used. Inspired by cognitive neuroscience that humans perceive the entirety from low-frequency information and discern details from high-frequency information, we devise a new framework for oriented infrared vehicle detection called infrared information mining detector (I2MDet) to tackle the above challenges. It consists of two significant designs: the kaleidoscope module and the semantic feature supplement module (SFSM). In the kaleidoscope module, we explore the effect of kernel sizes and dilation rates on frequency information mining with kaleidoscope-like equivalent kernels. Features in this module are extracted by adaptive involution operators instead of convolution kernels to deal with multiple patterns. The SFSM provides the network with features beneficial for classification and regression. On the one hand, the network is guided to learn more meaningful features under semantic supervision. On the other hand, features output by the SFSM supplement the detection head with semantic information. Experimental results on the public dataset DroneVehicle demonstrate that our proposed approach achieves outstanding performance on oriented infrared vehicle detection.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Discrimination Between Dry and Water Ices by Full Polarimetric Radar:
           Implications for China’s First Martian Exploration

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      Authors: Hai Liu;Jianhui Li;Xu Meng;Bin Zhou;Guangyou Fang;Billie F. Spencer;
      Pages: 1 - 11
      Abstract: China’s first Mars rover named “Zhurong” has begun its exploration at the surface of the Utopian Plain in the Martian northern hemisphere on May 22, 2021. Mars rover penetrating radar (RoPeR) is one of the key payloads onboard the Zhurong rover and one of its prima scientific objectives is to detect potential water ice and/or dry ice in the subsurface soil at the landing site. The high-frequency channel of RoPeR, which is equipped with a fully-polarized antenna array with a center frequency of 1.3 GHz, can record full polarimetric radar reflections from subsurface anomalies. In this article, a radar system with a polarimetric antenna array the same as RoPeR was set up and carefully calibrated. Laboratory experiments were carried out to test the feasibility of RoPeR in the detection and discrimination of potential dry ice and/or water ice in Martian soils. The experimental results indicate that the reflection signals from the bottom of the dry ice and water ice samples present different polarimetric scattering characterizations. The characteristic polarimetric scattering features of dry ice and water ice revealed in this article would guide the analysis and interpretation of the real data acquired by RoPeR on Mars.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Joint Design Methods of Unimodular Sequences and Receiving Filters With
           Good Correlation Properties and Doppler Tolerance

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      Authors: Fulai Wang;Xiang-Gen Xia;Chen Pang;Xu Cheng;Yongzhen Li;Xuesong Wang;
      Pages: 1 - 14
      Abstract: Sequence set design with good correlation properties and Doppler tolerance has been a classic and important problem in many multichannel systems, including but not limited to the multiple-input multiple-output (MIMO) and simultaneous polarimetric radar systems. In this article, we first consider the problem of jointly designing Doppler resilient unimodular sequences and receiving filters to minimize the metric weighted integrated sidelobe level (WISL), which can be used to construct sequence sets with “thumbtack” co-channel and zero cross-channel ambiguity functions (AFs). To control the signal-to-noise ratio (SNR) loss caused by the mismatched filter, a peak constraint function is added to the objective function based on the penalty function method. An algorithm based on the alternatively iterative scheme and general majorization-minimization (MM) method is developed to tackle the constrained joint design problem. Moreover, the proposed algorithm is then extended to optimize the ${l_{p}}$ -norm of sidelobes of AFs, which gives a way to optimize the weighted peak sidelobe level (WPSL) metric. Due to the use of the fast Fourier transform (FFT) algorithm and a general acceleration scheme, the proposed algorithm can be realized efficiently. A number of simulations are provided to demonstrate the excellent performance of the proposed sequence set synthesis algorithms. Besides, an application of using the designed Doppler resilient sequence set on the simultaneous polarimetric radar to detect multiple moving targets in a strong clutter scene is given via simulation, which further verifies the practicability of the proposed algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • DOD and DOA Estimation From Incomplete Data Based on PARAFAC and Atomic
           Norm Minimization Method

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      Authors: Sizhe Gao;Hui Ma;Hongwei Liu;Yang Yang;
      Pages: 1 - 14
      Abstract: In this article, we propose an efficient direction of departure (DOD) and direction of arrival (DOA) estimation method for bistatic multiple-input multiple-output (MIMO) radar with faulty arrays. A third-order tensor model is built, and the measurement 3-D structure can be better utilized than the traditional matrix model. Subsequently, the atomic norm minimization (ANM) technique is used to further improve the angle estimation performance. Furthermore, we found in the research process that when the faulty arrays still maintain the symmetry property, the measurement tensor can be converted to the real-valued domain by the forward–backward averaging technique and the unitary transform technique. The new algorithm we proposed exploits the multidimensional structure of the signal without estimating the signal subspace. Comparing with traditional matrix completion (MC) methods, it has a better performance in terms of robustness and resolving correlated targets. Also, the algorithm proposed in this article does not require angle pairing. Simulation results verify the effectiveness of the proposed algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Range-Spread Target Detection Based on Adaptive Scattering Centers
           Estimation

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      Authors: Zhouchang Ren;Wei Yi;Wenjing Zhao;Lingjiang Kong;
      Pages: 1 - 14
      Abstract: Proper prior knowledge of target scattering centers (SCs) can help to obtain better detection performance of range-spread targets. However, target SCs are sensitive to the target’s attitude relative to the radar and vary significantly among different targets. The existing approaches that employ predetermined prior knowledge may suffer performance degradation when the prior information does not match the practical scenarios. A possible way to circumvent this drawback is to estimate the SCs of different targets adaptively and check the presence of a target utilizing the range cells occupied by the most likely target SCs. For this reason, this article develops a generalized likelihood ratio test based on adaptive SCs estimation (ASCE-GLRT) for range-spread target detection in compound-Gaussian clutter. Under the assumption that the target SCs are sparse, we model the problem of SCs estimation as a sparse signal representation. Moreover, since the sparse assumption may not always be satisfied in practice, a modified sparsity regularization method is proposed to enhance the robustness of the estimation performance of targets with different scattering characteristics. A theoretical analysis shows that the proposed detector can achieve the constant false alarm rate (CFAR) property. The performance assessments conducted by numerical simulation and field tests confirm the effectiveness and robustness of the proposed detector.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiple Objects Automatic Detection of GPR Data Based on the AC-EWV and
           Genetic Algorithm

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      Authors: Guangyan Cui;Jie Xu;Yanhui Wang;Shengsheng Zhao;
      Pages: 1 - 16
      Abstract: The automatic detection of multiple objects in ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46%, 1.33%, and 0.36%, respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Space Target Classification With Corrupted HRRP Sequences Based on
           Temporal–Spatial Feature Aggregation Network

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      Authors: Yuan-Peng Zhang;Lei Zhang;Le Kang;Huan Wang;Ying Luo;Qun Zhang;
      Pages: 1 - 18
      Abstract: High-resolution range profile (HRRP) sequences have great potential for space target classification because they can provide both scattering information and micromotion information. However, many factors cause an obtained HRRP sequence for a space target to be corrupted in real cases due to noise interference, limited radar resources, and the requirement of multitarget observations. Many space target classification methods cease to be effective when HRRP sequences are corrupted, so classifying space targets with corrupted HRRP sequences is still a challenging problem. To solve this problem, a novel space target classification method based on a temporal–spatial feature aggregation network (TSFA-Net) is proposed by using the corrupted HRRP sequences directly. First, a sequence-to-token module (S2T-module) is designed to extract low-level and fine-grained features from the raw inputs. Second, to effectively model the long-range dependencies among corrupted HRRP sequences and capture global representations without losing target local features, we propose a parallel and dual-branch block, i.e., a temporal–spatial feature aggregation block (TSFA-block), by combining a Transformer network and a convolutional neural network (CNN). Then, via progressively hierarchically stacking TSFA-blocks, a hierarchical temporal–spatial feature aggregation subnetwork (H-TSFA-subnetwork) is constructed to obtain the final temporal–spatial features. Finally, a token-to-label module (T2L-module) is adopted to obtain the classification results. Extensive experiments demonstrate that the proposed method achieves state-of-the-art classification accuracy for space target classification with HRRP sequences, especially under the conditions of a low signal-to-noise ratio and a high missing rate.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Retrieval of Sea Surface Currents and Directional Wave Spectra by 24 GHz
           FMCW MIMO Radar

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      Authors: Giovanni Ludeno;Ilaria Catapano;Francesco Soldovieri;Gianluca Gennarelli;
      Pages: 1 - 13
      Abstract: This article investigates the capabilities of 24 GHz frequency-modulated continuous-wave (FMCW) multiple-input–multiple-output (MIMO) radar technology to retrieve sea surface currents and directional wave spectra. A procedure based on the dispersion relation, which was previously applied to process X-band marine radar data, is here exploited. The estimation performance of the radar sensor is first assessed by numerical tests in the case of synthetic sea wave fields with known characteristics in terms of wave direction and surface currents. Finally, the estimation procedure is assessed on real data collected at San Vincenzo quay in the port area of Naples, Italy. The achieved results are encouraging and highlight that 24 GHz FMCW MIMO radar is a viable technology for sea wave monitoring.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Enhancing Forward-Looking Image Resolution: Combining Low-Rank and
           Sparsity Priors

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      Authors: Junkui Tang;Zheng Liu;Lei Ran;Rong Xie;Jikai Qin;
      Pages: 1 - 12
      Abstract: Compressed sensing (CS)-based imaging technology has attracted a lot of interest because it can enhance imaging resolution. Targets of interest for forward-looking imaging radar are typically few in comparison to the entire imaging region. This sparsity allows for the natural application of CS to the reconstruction of high-resolution forward-looking images. However, conventional CS-based imaging methods can only perform well when the signal-to-noise ratio (SNR) is high. Strong noise in radar imaging prevents the CS-based methods from producing excellent imaging results. Inspired by the low-rank property of the received radar target echo and the sparsity of the forward-looking image targets, we present a combined low-rank and sparse prior restricted model for forward-looking imaging with a multichannel array radar to overcome strong noise. To solve the low-rank joint sparse double prior constraint optimization problem, an augmented Lagrange multiplier (ALM) reconstruction method under the framework of the alternating direction multiplier method (ADMM) is proposed. Finally, the results of simulation and real measurement data indicate that our presented method is fairly effective at enhancing the azimuth resolution and robustness of forward-looking radar imaging in comparison to other current methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Method for Staggered SAR Imaging in an Elevation Multichannel
           System

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      Authors: He Huang;Penghui Huang;Yanyang Liu;Huaitao Fan;Yunkai Deng;Xingzhao Liu;Guisheng Liao;
      Pages: 1 - 19
      Abstract: Synthetic aperture radar (SAR) is an advanced remote sensing technique, capable of observing Earth’s surface independent of weather conditions and sunlight illumination. Restricted by the minimum antenna area, however, conventional spaceborne SAR systems cannot achieve high azimuth resolution in a wide swath. In addition, blind ranges are present as the constant pulse repetition interval (PRI) is used. To solve these problems, a PRI-staggered elevation multichannel SAR (EMC-SAR) system is employed in this article. By transmitting the continuously PRI-varied sequence, the blind ranges are located at different regions in different receive instants, effectively avoiding the loss of coverage in elevation. In this system, three issues are required to be addressed: 1) recovering the missed data located at blind ranges; 2) suppressing range ambiguous components; and 3) restoring the PRI-varied signal into a regular grid. To deal with these problems, we propose a novel SAR imaging method for a PRI-staggered EMC-SAR system. To be applied on-ground, assume downlinking of the individual elevation channels. First, the modified $varepsilon $ -insensitive loss tube regression with the L2 regularization method is applied to recover the missed data. Then, the range ambiguous components are suppressed by performing digital beamforming (DBF) based on the elevation multichannel technique, where the covariance matrix is constructed by using an iterative adaptive algorithm. After that, a generalized scaling transform is employed to restore the PRI-varied signal into a uniform sampled grid. Finally, a well-focused SAR image can be obtained by performing the conventional SAR imaging techniques. The effectiveness of the proposed method is validated by both simulated and real SAR data processing results.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A System Optimization Scheme for Bias Correction of Polarimetric
           Phased-Array Radar

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      Authors: Yaomin He;Tao Zhang;Huafeng He;Junjun Yin;Jian Yang;
      Pages: 1 - 17
      Abstract: With the change in the spatial angle, the cross-polarization isolation (CPI) of polarimetric phased-array radar (PPAR) changes as well, destroying the estimation of the target polarization scattering matrix (PSM). To correct the bias in PPAR, this article comprehensively designs the transmitting antenna, receiving antenna, and signal waveform and proposes a bias correction method based on system optimization. First, for the transmitting antenna of PPAR, the second-order cone program (SOCP) model is proposed to optimize the weighting coefficient. With the SOCP-based beamforming method, not only beam pattern in any spatial angle can be achieved, but also arbitrary polarization state can be precisely configured. Then, for the wideband receiving signal with a certain beamwidth, an angle estimation method based on eigenvalue decomposition is proposed in this article, which can effectively cure the challenges introduced by the beamwidth and signal bandwidth. Subsequently, for the signal waveform, the phase code is designed to measure all the elements of PSM in simultaneous transmission and simultaneous reception (STSR) mode, which could eliminate the biases of the moving speed and the second-order cross-polarization error. Finally, this article compares with other methods based on differential reflectivity, and experiments show that the factors such as the spatial angle, array structure, antenna beamwidth, signal bandwidth, motion speed, and signal-to-noise ratio (SNR) have the least influence on the present method in this article.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multi-UAV Collaborative Trajectory Optimization for Asynchronous 3-D
           Passive Multitarget Tracking

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      Authors: Jinhui Dai;Wenqiang Pu;Junkun Yan;Qingjiang Shi;Hongwei Liu;
      Pages: 1 - 16
      Abstract: This article considers the 3-D collaborative trajectory optimization (CTO) of multiple unmanned aerial vehicles to improve multitarget tracking performance with an asynchronous angle of arrival measurements. The predicted conditional Cramér–Rao lower bound is adopted as a performance measure to predict and subsequently control tracking error online. Then, the CTO problem is cast as a time-varying nonconvex problem subjected to constraints arising from dynamic and security (height, collision, and obstacle/target/threat avoidance). Finally, a comprehensive solution method (CSM) is presented to tackle the resulting problem, according to its unique structures. Specifically, if all security constraints are inactive, the CTO can be simplified as a nonconvex problem with convex dynamic constraints, which can be solved by the nonmonotone spectral projected gradient (NSPG) method. Oppositely, an alternating direction penalty method (ADPM) is presented to solve the CTO problem with some positive security constraints. The ADPM introduces auxiliary vectors to decouple the complex constraints and separates the CTO into several subproblems and tackles them alternately, while locally adjusting the penalty factor at each iteration. We show the subproblem w.r.t. the position vector is nonconvex but with convex constraints, which can be efficiently solved by the NSPG method. The subproblems w.r.t. the auxiliary vectors are separable and have closed-form solutions. Simulation results demonstrate that the CSM outperforms the unoptimized method in terms of tracking performance. Besides, the CSM achieves the near-optimal performance provided by the genetic algorithm with much lower computational complexity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Inversion of Wave Parameters From Time-Doppler Spectrum Using Shore-Based
           Coherent S-Band Radar

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      Authors: Chen Zhao;Xiao Wang;Zezong Chen;Sitao Wu;Yichen Zeng;
      Pages: 1 - 11
      Abstract: For coherent microwave radar, the Bragg scattering from the broken-short waves generated after wave breaking usually introduces extra low-frequency components in the estimated wave height spectrum and, thus, leads to inaccurate retrievals of wave parameters, especially the overestimation of wave period. In order to eliminate the impacts of wave breaking, some methods based on the removal of “group line” in the spatial–temporal domain are proposed to estimate wave parameters. However, these methods are not suitable for the case that the spatial–temporal data are not available. To address this problem, a method is proposed to invert wave parameters only from the time-Doppler spectrum. Temporal velocity series are derived from the time-Doppler spectrum in which breaking components are removed. Then, the wave height spectrum from which wave parameters can be obtained is estimated from the velocity series by the direct transform relationship based on the linear wave theory. Without spatial–temporal data, the “group line” can be removed using the proposed method, and the method is validated by simulation. In addition, an approximately 11-day dataset collected with a shore-based coherent S-band radar deployed along the coast of Zhejiang province in China is reanalyzed and used to retrieve significant wave height and mean wave period. Compared with the buoy-measured data, the significant wave heights and mean wave periods retrieved by the proposed method have the root-mean-square differences (RMSDs) of 0.25 m and 0.60 s, respectively, and also have the correlation coefficients (CCs) of 0.94 and 0.81, respectively. The results indicate that the proposed method can invert wave parameters from the time-Doppler spectrum with a reasonable performance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Marine Radar Small Target Classification Based on Block-Whitened
           Time–Frequency Spectrogram and Pre-Trained CNN

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      Authors: Shuwen Xu;Hongtao Ru;Dongchen Li;Penglang Shui;Jian Xue;
      Pages: 1 - 11
      Abstract: This article presents a classification method to classify different marine floating small targets, which can realize effective classification of different targets in strong clutter background. The design of proposed classification method is primarily based on block-whitened time–frequency spectrogram and pre-trained convolution neural network (CNN). Block-whitening clutter suppression is used to process target echoes. By converting a strong clutter background to an approximately noisy background, the effect of strong clutter on classification is reduced. Then, the time–frequency spectrogram of targets is extracted from the block-whitened target echoes, which converts a signal in time domain into a time–frequency spectrogram with more information. In addition, the block-whitened time–frequency spectrograms are input to a pre-trained CNN for feature extraction and classification training. By exploiting pre-training procedure, the proposed method can effectively classify different marine floating small targets and solve the problem of limited target samples in practical applications. Finally, a dataset of three kinds of measured maritime radar targets is constructed to verify the effectiveness of proposed method. Experimental results show that compared with competitors, the pre-trained CNN with block-whitened time–frequency spectrograms can achieve higher performance on the measured dataset.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Trihedral Corner Reflector for Radar Altimeter Calibration

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      Authors: Ferran Gibert;Adrià Gómez-Olivé;Albert Garcia-Mondéjar;Richard Francis;Sergi Hernández;Adrián Flores de la Cruz;Ester Vendrell;Mònica Roca i Aparici;
      Pages: 1 - 8
      Abstract: A trihedral corner reflector has been used to evaluate the capability of passive reflectors to calibrate radar altimeters, such as the Poseidon-4 altimeter on board Sentinel- 6A. The reflector location, placed on the top of a mountain ridge and about 4-km off-nadir of the Sentinel- 6A subsatellite track, allows capturing echoed signals with a signal-to-clutter ratio (SCR) around 40 dB when processing the received data with a fully-focused synthetic aperture radar (FF-SAR) algorithm. Results obtained show a range bias of 33.9 with 8.5 mm of standard deviation and datation bias of $- 2.3,,mu text{s}$ with a standard deviation of $1.8~mu text{s}$ for the measurement campaign between September 2021 and April 2022. Such values are comparable to what is currently achieved by means of active transponders, and therefore, it is demonstrated that passive reflectors may be of interest to support radar altimeter regular calibration.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep-Learning-Based Flying Animals Migration Prediction With Weather Radar
           Network

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      Authors: Huafeng Mao;Cheng Hu;Rui Wang;Kai Cui;Shuaihang Wang;Xiao Kou;Dongli Wu;
      Pages: 1 - 13
      Abstract: Monitoring and forecasting aerial animal migration benefit biological conservation, aviation safety, and agricultural production. Due to the lack of large-scale observation data and quantitative knowledge of aerial animal migration mechanisms, it is difficult to build a numerical simulation system for migration prediction. However, the extensive deployment of weather radars makes it possible to obtain large-scale aerial migration information. Meanwhile, artificial intelligence technologies provide new insights into the modeling of complex system. In this article, we develop a deep-learning model to predict aerial migration from the perspective of spatio-temporal evolution. Specifically, an undirected graph is applied to describe the geographic structure of the weather radar network, and then graph convolution and gated recurrent unit (GRU) are combined to extract spatio-temporal features of migration information. In addition, a multi-head self-attention mechanism is applied to enhance long-term dependence. Experiments are conducted to validate the effectiveness of the proposed model on the data from the Chinese weather radar network. The results show that our model can achieve state-of-the-art performance among the competing methods. Moreover, improvements from graph convolution and multi-head self-attention are also analyzed. In future applications, more weather radar data will be collected to enrich the dataset and build an aerial migration monitoring and prediction system.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Approach for Radar Passive Jamming Based on Multiphase Coding
           Rapid Modulation

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      Authors: Heng Xu;Yinghui Quan;Xiaoyang Zhou;Hui Chen;Tie Jun Cui;
      Pages: 1 - 14
      Abstract: Electromagnetic controlled surface (ECS) which can regulate the amplitude and phase of electromagnetic wave reflection characteristics has attracted extensive attention in recent years because of its radar stealth effect by reducing the radar cross Section (RCS). However, radar detection cannot be avoided only by stealth in the energy field gradually. It is a novel pulse Doppler radar jamming mode proposed in this article to use the self-correlation of radar-transmitted waveforms to result in signal processing and radar detection failure through the rapid time-domain variation of phase coding ECS. In principle, phase modulation alters the intrapulse characteristics of the original signal and fundamentally interferes with the processing of the radar signal. In this article, random and periodic phase coding sequences are proposed according to different forms of radar jamming. And through the derivation formula and simulation experiment, the jamming effect under multiphase modulation is verified. Among them, the target energy is dispersed to the surroundings to form a wide envelope through random coding modulation, which leads to noise barrage jamming. While we use periodic sequence coding modulation, the target is shifted in the range–Doppler domain, causing misplaced coherence on the radar echo and deceptive jamming to the radar. Moreover, the technology can also be widely used in synthetic aperture radar (SAR) imaging, microwave measurement, and other remote sensing fields. To further confirm the accuracy and efficiency of the proposed method, multiparameter contrast experiments and parameter sensitivity analyses are conducted.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Sparse Aperture Autofocusing and Imaging Based on Fast Sparse Bayesian
           Learning From Gapped Data

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      Authors: Yuanyuan Wang;Fengzhou Dai;Qian Liu;Ling Hong;Xiaofei Lu;
      Pages: 1 - 16
      Abstract: Sparse aperture (SA) autofocusing and imaging is a hot research problem in the signal processing field and has been widely used. Under SA, the absence of echoes destroys the coherence between the pulses, which then affects the autofocusing accuracy of the imaging, leading to defocus of the image. In this article, a novel SA autofocusing and imaging algorithm based on sparse Bayesian learning (SBL) is proposed, which uses a fast SBL algorithm to achieve SA high-resolution imaging and the minimum Tsallis entropy algorithm to realize autofocusing. As is known to all, SBL has strong robustness and high precision. Unfortunately, the direct calculation of the inversion and multiplication operations involved in each iteration of SBL results in significant computational costs. In the proposed fast SBL algorithm, the matrix required to be inverted has a special structure. The inverse matrix can then be represented by Gohberg–Semencul (G–S) factorization. Also, almost all operations except for G–S factorization during each iteration can be completed by fast Fourier transform (FFT) or inverse FFT (IFFT), which greatly reduces the amount of computation by several orders of magnitude. In each SBL iteration, the minimum Tsallis entropy algorithm is used for estimating the phase error, which has better noise sensitivity and obtains the images with the best focused degree. Finally, the effectiveness and high efficiency of the proposed fast algorithm are verified by experimental results obtained by simulation and measured data.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter

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      Authors: Xiaoqiang Hua;Linyu Peng;Weijian Liu;Yongqiang Cheng;Hongqiang Wang;Huafei Sun;Zhenghua Wang;
      Pages: 1 - 15
      Abstract: This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of the received dataset is estimated as the geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, which are elements of a higher dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher dimensional HPD matrix manifold to a lower dimensional one subject to maximum discrimination. In this study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA and the state-of-the-art maritime target detection methods in nonhomogeneous sea clutter.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • NLOS Positioning for Building Layout and Target Based on Association and
           Hypothesis Method

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      Authors: Peilun Wu;Jiahui Chen;Shisheng Guo;Guolong Cui;Lingjiang Kong;Xiaobo Yang;
      Pages: 1 - 13
      Abstract: Localization of non-line-of-sight (NLOS) targets in the complex urban environment has attracted significant attention in recent years. However, the requirement for precise prior information about the environment is idealistic. It is challenging to know the environmental information in the blind area of vision in advance of practical applications. This article proposes a joint estimation algorithm for building layout and target position in the L-shaped scene without any prior information. Specifically, a round-trip multipath propagation model is first developed for the cases of diffraction and multiple reflections. Then, the received echo signal is preprocessed with moving target identification (MTI), back-projection (BP) imaging, and image segmentation. In addition, the target points, which are screened by the geometric association, are further matched and estimated by the multipath ghost’s hypothesis method, thus realizing the joint perceptual estimation of the building layout and the target position. Finally, electromagnetic (EM) simulations and experimental measurements are used to validate the effectiveness of the proposed algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Track-to-Track Association Based on Maximum Likelihood Estimation for
           T/R-R Composite Compact HFSWR

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      Authors: Weifeng Sun;Xiaotong Li;Zhenzhen Pang;Yonggang Ji;Yongshou Dai;Weimin Huang;
      Pages: 1 - 12
      Abstract: Due to its low transmit power and reduced aperture size of a receiving antenna array, compact high-frequency surface wave radar (HFSWR) suffers from low detection probability, low positioning accuracy, and high false alarm rate. In a multitarget tracking scenario, similar kinematic parameters of adjacent targets raise challenges to the track-to-track association procedure. Taking the measurement uncertainty of compact HFSWR into consideration, a track-to-track association method based on maximum likelihood estimation (MLE) for T/R-R composite compact HFSWR is proposed. First, a multitarget tracking algorithm is applied to plot data sequences acquired by both T/R monostatic and T-R bistatic radars to produce two track sets. Then, the measurement errors of range, azimuth, and Doppler velocity are calculated using the obtained radar track and corresponding automatic identification system (AIS) track data, and a Gaussian distribution model is derived through probability distribution fitting. Subsequently, likelihood functions are established using the obtained Gaussian distribution model to calculate the association cost of tracks for T/R monostatic and T-R bistatic radars, and a cost matrix is obtained. Finally, the Jonker–Volgenant–Castanon (JVC) assignment algorithm is applied to the cost matrix to determine associated track-to-track pairs. Track-to-track association experiments using both simulated and field data were conducted, and the association performance of the proposed method is compared with that of Mahalanobis distance-based nearest neighbor (NN) method. Experimental results demonstrate that the proposed method can effectively resolve association ambiguity and achieve correct track-to-track association in track crossing and adjacent multitarget scenarios.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Attenuation Correction in Weather Radars for Snow

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      Authors: Shashank S. Joshil;V. Chandrasekar;
      Pages: 1 - 14
      Abstract: Weather radars play a prominent role in remote sensing of the atmosphere. Various fields, such as meteorology and hydrology, rely on accurate weather radar data as input for their models. Different hydrometeors present during a weather event influence the amount of attenuation encountered by the radar signal. Attenuation correction for dual-polarization weather radar data is necessary to improve the radar products and get accurate measurements. Most of the existing attenuation correction research is associated with rain hydrometeors. Currently, research that addresses the attenuation correction of snow in weather radars is limited. Although it is known that attenuation of radar signals when it encounters rain is much greater than that for snow, attenuation for all hydrometeors needs to be addressed for accurate radar estimates. In this research work, the attenuation of different hydrometeors is studied using signal simulations. Various factors which influence attenuation, such as the elevation angle and particle size distribution, are considered, and the results are presented. An attenuation correction algorithm that uses the hydrometeor classification and specific differential phase products from the DROPS2.0 algorithm is introduced. Signal simulations are employed to obtain the relationship between specific attenuation and specific differential phases for different hydrometeors used in the proposed algorithm. The attenuation correction method is applied to X-band and Ku-band radar data. Path-integrated attenuation of about 8 dB was observed in the snow case discussed from Ku-band radar data. The method proposed for attenuation shows promising results at both frequency bands.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Joint Design of Transmit Sequence and Receive Filter Based on Riemannian
           Manifold of Gaussian Mixture Distribution for MIMO Radar

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      Authors: Xixi Chen;Hao Wu;Yongqiang Cheng;Weike Feng;Yifan Guo;
      Pages: 1 - 13
      Abstract: To improve target detection performance in non-Gaussian backgrounds, the joint design of transmit sequence and receive filter for multiple-input–multiple-output (MIMO) radar is studied. By approximating the probability density function of observed non-Gaussian data with the Gaussian mixture model (GMM), a Riemannian manifold of Gaussian mixture distribution is developed to depict the complicated background first. Then, maximizing the geometric distance on manifolds, which is converted by maximizing the discrimination between the target and clutter, is proposed as the criterion for the joint design of transmit sequence and receive filter. Thereby, under the constant-modulus constraint, the joint design problem can be transformed into an optimization problem. However, the proposed optimization problem is nonconvex and constrained. To solve this problem, a Riemannian optimization framework is provided. By taking the advantage of the underlying geometric and algebraic structure of the constraint space, the original constrained optimization problem in Euclidean space can be transformed into the unconstraint optimization problem over Riemannian product manifolds. Moreover, to obtain the global optimal solution, the Riemannian gradient of the geometric distance cost is derived for the conjugate gradient algorithm. Experiments demonstrate that the proposed method shows advantages in detection performance compared with competitive methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Variable Length Sequential Iterable Convolutional Recurrent Network for
           UWB-IR Vehicle Target Recognition

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      Authors: Di Chen;Gang Xiong;Lizhe Wang;Wenxian Yu;
      Pages: 1 - 11
      Abstract: A variable length sequential iterable convolutional recurrent network (VS-ICRN) is proposed in this article, aiming at improving the vehicle target recognition ability for the ultrawideband impulse radar (UWB-IR). First, the array imaging technology is introduced into the UWB-IR, and thus, a range-angle imaging method for the array UWB-IR is put forward, to simulate the array UWB-IR vehicles image under different observation conditions. Second, in order to make full use of both the deep features in the single image and the deep associated features between the sequence images, a VS-ICRN model is proposed, which includes three submodules: the image feature extraction based on the iterable convolution, the variable length sequential image associated feature extraction, and the target classification. Finally, the experiment on the simulation dataset and the Moving and Stationary Target Acquisition and Recognition (MSTAR) is carried out to validate the effectiveness of the proposed method. The experimental results on the simulation dataset show that when SNR $= -10$ dB, the proposed method is superior in the recognition rate to the GoogLeNet and AlexNet methods with 16% and 19%, respectively. Meanwhile, the proposed VS-ICRN method only needs 1.38% parameters quantity to achieve a comparable recognition rate as GoogLeNet on the MSTAR dataset.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Radar HRRP Open Set Recognition Based on Extreme Value Distribution

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      Authors: Ziheng Xia;Penghui Wang;Ganggang Dong;Hongwei Liu;
      Pages: 1 - 16
      Abstract: Radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) has attracted more attention in recent years. In fact, the actual application environment of RATR is open set environment rather than closed set environment. However, previous works mainly focus on closed set recognition, which classifies the known classes by dividing hyperplanes in the feature space, and it will cause classification errors in an open set environment. Therefore, open set recognition is proposed to solve this problem, which needs to determine a closed classification boundary for the identification of the known and unknown targets simultaneously. To accomplish this purpose, this article proposes and proves the extreme value boundary theorem, which demonstrates that the maximum distance from the known features to the cluster center follows the generalized extreme value distribution. According to the proposed theorem, the closed classification boundary of the cluster is easily determined to distinguish between the known and unknown classes. Finally, extensive experiments on measured HRRP data verify the validity of the proposed theorem and the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • An End-to-End Approach for Rigid-Body Target Micro-Doppler Analysis Based
           on the Asymmetrical Autoencoding Network

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      Authors: Fengzhou Dai;Jiang Liu;Long Tian;Hang Dong;Ling Hong;
      Pages: 1 - 19
      Abstract: Micro-Doppler analysis (MDA) of the rigid-body target is significant for attitude estimation and recognition of space objects. The traditional MDA method for rigid-body targets includes two sequential steps. First, the time–frequency analysis is performed on the radar echo data of the target, and then, the micro-Doppler curve of each scattering center is separated and extracted from the time–frequency map. The second step depends on the micro motion model of the target. In the MDA of the real rigid-body target, there are some problems, such as the mismatch of the micro motion model, the incidence angle dependence of the scattering center position, and the partial occlusion of the scattering center. Therefore, it is very difficult to correctly extract the micro-Doppler curves of multiple scatterers. In this article, an end-to-end MDA method for rigid-body targets based on a deep learning network is proposed, which can directly separate and extract the micro-Doppler curves of multiple scatterers from the target echo data. Specifically, an asymmetrical autoencoding (A2E) network equipped with a data preprocessing (DP2) module is developed to extract time–frequency curves (TFCs) from radar echoes. Considering the sparseness of time–frequency distribution (TFDs), we then develop a novel energy-concentration objective (ECO) function based on min–max game to enhance curves energy while suppressing the background energy. In practice, measured TFDs are rarely annotated; they restrict the generalization capability of the A2E from the simulation to the measurement. To bridge the gap, twofold modifications are finally constructed: 1) we insert a partial-shared branch of the decoder to reconstruct the TFD from the DP2 module and 2) we regularize the ECO function with a knowledge preservation-based reconstruction bound to further capture the characteristics of measured echo-s in a semisupervised way at test time to relieve the domain-shift problem.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Computationally Efficient Airborne Forward-Looking Super-Resolution
           Imaging Method Based on Sparse Bayesian Learning

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      Authors: Weixin Li;Ming Li;Lei Zuo;Hongmeng Chen;Yan Wu;Zhenyu Zhuo;
      Pages: 1 - 13
      Abstract: In airborne forward-looking imaging, the azimuth resolution and the imaging efficiency are important. In this article, we propose a low-dimensional sparse Bayesian learning with Doppler compensation (LDSBL-DC) method to improve the azimuth resolution with low computational complexity in airborne forward-looking imaging. First, since the variant pitching angle causes the space variant of the Doppler centroid, the Doppler convolution matrix needs to be constructed in each range cell. We construct a Doppler compensation matrix to eliminate the space variant of the Doppler centroid. After the Doppler centroid compensation, the Doppler convolution matrix only needs to be constructed once. Second, we propose a low-dimensional projection model based on the singular value decomposition. In the low-dimensional projection model, the high-dimensional echo data is compressed to low-dimensional data. Finally, combining Doppler centroid compensation and low-dimensional projection model, a new forward-looking imaging model is created, and we introduce sparse Bayesian learning (SBL) to estimate the imaging parameters. In the estimation of the targets’ scattering coefficient, we reduce the computational complexity by the matrix transformation. Several simulations are designed to evaluate the performance of the efficient forward-looking imaging method. The simulation results show that the LDSBL-DC method can improve the azimuth resolution with a low computational complexity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Learning for Feature Matching via Graph Context Attention

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      Authors: Junwen Guo;Guobao Xiao;Zhimin Tang;Shunxing Chen;Shiping Wang;Jiayi Ma;
      Pages: 1 - 14
      Abstract: Establishing reliable correspondences via a deep learning network is an important task in remote sensing, photogrammetry, and other computer vision fields. It usually requires mining the relationship among correspondences to aggregate both local and global contexts. However, current methods are insufficient to effectively acquire context information with high reliability. In this article, we propose a graph context attention-based network (GCA-Net) to capture and leverage abundant contextual information for feature matching. Specifically, we design a graph context attention block, which generates multipath graph contexts and softly fuses them to combine respective advantages. In addition, for building the graph context containing stronger representation ability and outlier resistance ability, we further design a local–global channel mining block to gather context information by focusing on the significant part as well as to mine dependencies among channels of correspondences in both local and global aspects. The proposed GCA-Net is able to effectively infer the probability of correspondences being inliers or outliers and estimate the essential matrix meanwhile. Extensive experimental results for outlier removal and relative pose estimation demonstrate that GCA-Net outperforms the state-of-the-art methods on both outdoor and indoor datasets (i.e., YFCC100M and SUN3D). In addition, experiments extended to remote sensing and point cloud scenes also demonstrate the powerful generalization capability of our network.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Estimation of Micro-Doppler Parameters With Combined Null Space Pursuit
           Methods for the Identification of LSS UAVs