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IEEE Transactions on Geoscience and Remote Sensing
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 208  
 
  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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • 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)
       
  • A Generalized Geodesic Distance-Based Approach for Analysis of SAR
           Observations Across Polarimetric Modes

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      Authors: Debanshu Ratha;Andrea Marinoni;Torbjørn Eltoft;
      Pages: 1 - 16
      Abstract: Present and future sensors are diversifying from traditional quad polarimetric mode of synthetic aperture radar acquisition. Thus, an approach that is interpretative in nature and applicable across polarimetric modes is required. In this context, the geodesic distance (GD)-based approach within the polarimetric synthetic aperture radar (PolSAR) literature is seen as an eigenvalue-decomposition free approach to interpret and analyze quad PolSAR data. This approach is highly adaptive toward applications due to its ability to compare the SAR observation with a known scatterer/model, or with another SAR observation in general providing a means for direct interpretation. In this work, we show that the GD (originally defined for the quad polarization mode) is generalizable across any arbitrary SAR polarimetric mode while retaining its simple form for ready computation. We show that the GD-based approach provides level ground for comparison of different polarimetric modes given a fixed application. We demonstrate it using change detection as the chosen application. In addition, we show how the behavior of the three roll-invariant GD-based parameters change under different polarimetric modes (e.g., quad, dual, and compact polarization modes). We also discuss how the GD-based approach can also be adapted to ground range detected (GRD) product data, which is presently available from Sentinel-1 and widely used in many applications. However, in this case, we show that only one of the three GD-derived parameters can be defined. We believe this work will make the GD-based approach important for present and future PolSAR applications cutting across sensors and its available polarimetric modes.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Waveform Design for Watermark Framework Based DFRC System With Application
           on Joint SAR Imaging and Communication

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      Authors: Jing Yang;Youshan Tan;Xianxiang Yu;Guolong Cui;Di Zhang;
      Pages: 1 - 14
      Abstract: In this article, the watermarking framework for electromagnetic systems is established with nonblind, semiblind, and blind watermarking demodulation processes mitigated to applications like radar detection, synchronization, integrated dual function, etc. Desired for similar advantages and trade-offs of watermarking technology, the dual function radar and communication (DFRC) system is specifically concerned for covert communication and low possibility of interception (LPI) radar sensing. In this respect, we propose a novel DFRC waveform design method where peak sidelobe level (PSL) of autocorrelation function (ACF) is considered as the figure of merit with information embedded via discrete Fourier transform (DFT) watermarking strategy. Meanwhile, the peak-to-average ratio (PAR) and energy constraints are forced to ensure compatibility with current hardware technique. To handle the resulting NP-hard design problems, the proximal method of multipliers (PMM) is employed with the overall computational burden linear with the amount of information per pulse and quadratic with respect to the code length. Finally, numerical and experimental results are provided to evaluate the effectiveness of the proposed DFRC waveform design scheme with application in joint synthetic aperture radar (SAR) imaging and communication.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Parameter-Free Enhanced SS&E Algorithm Based on Deep Learning for
           Suppressing Azimuth Ambiguities

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      Authors: Yuxi Suo;Kun Fu;Youming Wu;Wenhui Diao;Wei Dai;Xian Sun;
      Pages: 1 - 16
      Abstract: Aliasing artifacts introduced by azimuth ambiguity seriously impact the interpretation of synthetic aperture radar images. To achieve parameter-free and fast azimuth ambiguity suppression, a novel deep learning model is designed to estimate the ambiguous signal intensity to total signal intensity ratio in the range-Doppler domain. This model does not depend on processing parameters and can be applied in any acquisition mode. The mean shift algorithm is applied to select less ambiguous subspectra according to the estimation result. The selected subspectra are restored to a full spectrum with an energy concentrated extrapolation method to preserve the resolution. The enhanced spectral selection and extrapolation algorithm overcomes the dependence on processing parameters, and experiments based on TerraSAR-X and Radarsat-2 images indicate that the proposed algorithm suppresses the azimuth ambiguity significantly.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • SAR Imaging in Frequency Scan Mode: System Optimization and Potentials for
           Data Volume Reduction

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      Authors: Nicola Gollin;Rolf Scheiber;Michele Martone;Paola Rizzoli;Gerhard Krieger;
      Pages: 1 - 20
      Abstract: Frequency scanning (FScan) is an innovative acquisition mode for synthetic aperture radar (SAR) systems. The method is based on the frequency-dependent beam pointing capabilities of phased array antennas, artificially increased via the combined use of true time delays and phase shifters within the array antenna. By this, typical limitations of conventional SAR systems in terms of achievable swath width and azimuth resolution can be mitigated, and so a wide swath can be imaged maintaining a fine azimuthal resolution. In the first part of the article, we introduce the theoretical concept, which is necessary to evaluate the reduced echo window length (EWL) with respect to equivalent stripmap data and the implications for the transmit pulse characterization. An FScan sensor flying in a TerraSAR-X-like orbit is shown to be capable of imaging an 80-km wide swath with 1-m azimuth resolution. The resulting time–frequency properties of the recorded raw data make the traditional SAR data compression algorithms such as block-adaptive quantization (BAQ) highly inefficient in this case. Therefore, the second part of the article investigates dedicated quantization methods for efficient data volume reduction in FScan systems. Different solutions are investigated and evaluated through simulations. Various transformations of the raw data have been exploited to optimize the encoding process, including deramping, fast Fourier transform (FFT), and blockwise approaches. Compared with standard BAQ in the time domain, the suggested data compression methods significantly improve the resulting signal-to-quantization noise ratio, allowing for the reduction in the overall data volume by about 60% for the considered system scenario, while maintaining robustness in the presence of inhomogeneous scene characteristics at the cost of a modest complexity increase for its on-board implementation.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Moving Targets Detection for Video SAR Surveillance Using Multilevel
           Attention Network Based on Shallow Feature Module

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      Authors: He Yan;Xing Xu;Guodong Jin;Qianru Hou;Zhe Geng;Ling Wang;Jindong Zhang;Daiyin Zhu;
      Pages: 1 - 18
      Abstract: In this article, a novel method for the moving target detection through multilevel spatial and channelwise attention network based on shallow feature channel module (MSCA-SFCM) is presented, and the circular spotlight (CSL) video synthetic aperture radar ground moving target indication (Video-SAR-GMTI) mode of the Nanjing University of Aeronautics and Astronautics miniature SAR (NUAA MiniSAR) system is introduced. However, due to the lack of moving target samples, MSCA-SFCM cannot be directly applied to the CSL Video-SAR-GMTI mode in the real system. To this end, this article proposes a training sample library construction scheme for moving targets of high verisimilitude. In this scheme, based on the radar system parameters, after the traversal of moving target parameters and SAR imaging, the scattering line characteristic of all possible moving targets under the current system parameters is simulated and then used for MSCA-SFCM network training. Afterward, the properly trained network can be used for moving target detection in real radar data. The effectiveness of the proposed method is verified by the NUAA MiniSAR system.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Resolution Enhancement for Forwarding Looking Multi-Channel SAR Imagery
           With Exploiting Space–Time Sparsity

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      Authors: Jingyue Lu;Lei Zhang;Shaopeng Wei;Yachao Li;
      Pages: 1 - 17
      Abstract: Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is of the capability to achieve unambiguous 2-D images in the forward-looking slight direction. FLMC-SAR imagery usually suffers from relatively low spatial resolution as only limited Doppler diversity can be generated from the synthetic aperture. In this article, a sparsity-driven resolution enhancement algorithm is proposed to improve the resolution FLMC-SAR image of the forward-looking area. Different from conventional beamforming processing to resolve the FLMC-SAR left–right ambiguity, a Bayesian sparsity reconstruction optimization is developed for jointly ambiguity resolving and resolution enhancement in the azimuth angle image domain. The spatial structure of the target in the preliminary image domain is used as the signal sparsity with prior information to solve the constrained optimization problem for FLMC-SAR image resolution enhancement. A local least square estimator of the prior noise and signal statistics in the FLMC-SAR nonisotropic image is established in terms of determining the sparsity weight parameter. Extensive simulation and real FLMC-SAR data experiments confirm that the proposed algorithm is capable of achieving the unambiguous and resolution-enhanced FLMC-SAR image.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Ground Moving Target Detection With Nonuniform Subpulse Coding in SAR
           System

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      Authors: Xiongpeng He;Yue Yu;Yifan Guo;Guisheng Liao;Shengqi Zhu;Jingwei Xu;Tong Gu;
      Pages: 1 - 18
      Abstract: For the high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, the increasing imaging width results in a serious range ambiguity problem, which affects the performance of ground moving target indication (GMTI). In this article, a novel nonuniform subpulse coding (NSPC) scheme is proposed. It is characterized by resorting to range-frequency band resources and detailed coding design for each subpulse, enabling the beam auto-scanning in elevation. Also, the bandpass filtering and digital beamforming (DBF) technology with improved data reconstruction are utilized to realize the separation of subpulses and suppress range ambiguity. The NSPC technique exchanges the signal bandwidth for increasing swath without range ambiguity, and the coded subpulses can be directed to the prescribed regions, while skipping the invalid areas where the echoes are blocked. After that, through the robust principal component analysis (RPCA) method, the moving target detection is performed for each separated region without residual range-ambiguous interference. The proposed approach has been theoretically deduced in detail and the simulation experiments demonstrate its effectiveness.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • What Catch Your Attention in SAR Images: Saliency Detection Based on
           Soft-Superpixel Lacunarity Cue

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      Authors: Fei Ma;Xuejiao Sun;Fan Zhang;Yongsheng Zhou;Heng-Chao Li;
      Pages: 1 - 17
      Abstract: In existing superpixel-wise saliency detection algorithms, superpixel generation often is an isolated preprocessing step. The performance of saliency maps is determined by the accuracy of superpixels to a certain extent. However, it is still a challenge to develop a stable superpixel generation method. In this article, we attempt to incorporate the superpixel generation and saliency calculation steps into an end-to-end trainable deep network. First, we employ a recently proposed differentiable superpixel generation method to over-segment the synthetic aperture radar (SAR) images, which outputs the possibility that the pixels assigned to neighbor superpixels (soft superpixel). In saliency calculation part, as one of our main contributions, we propose a differentiable and computationally simple saliency model, i.e., lacunarity cue. It is inspired by the fact that generally the backscattering intensity of regions of interest (ROIs) in SAR images irregularly fluctuates, while the areas with consistent pixels are often ignored as the clusters. We improve the pixelwise box differential dimension algorithm to measure the irregularity of scattering points in a superpixel. The superpixel generation and saliency calculation can be implemented under a unified deep network. Hence, the shapes of the superpixels can be iteratively adjusted according to the saliency maps until the ROIs are correctly detected. Experiments on real SAR images with different sizes and scenes show that the saliency maps can effectively highlight the target areas, thus outperforming the state-of-the-art saliency detection models.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Monostatic-Equivalent Algorithm via Taylor Expansion for BiSAR Ship Target
           Imaging

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      Authors: Guangzhao Qian;Yong Wang;
      Pages: 1 - 19
      Abstract: It is well known that the bistatic synthetic aperture radar (BiSAR) can obtain high-resolution imaging in the forward direction of radar, which is not possible with the conventional monostatic SAR systems. However, most studies on BiSAR imaging mainly focus on stationary targets, and the research on moving targets and ship targets is relatively scarce. In this article, a monostatic-equivalent (ME) algorithm via Taylor expansion for BiSAR imaging of ship target is proposed. The monostatic equivalence method based on Taylor expansion is adopted to approximate BiSAR to monostatic model; besides, the method based on the midpoint of baseline is also analyzed for comparison. Then, the hybrid SAR-ISAR technology is utilized for ship target imaging under the ME model. Finally, the experimental results are given to substantiate the effectiveness and robustness of the proposed algorithm.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Unpaired Speckle Extraction for SAR Despeckling

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      Authors: Huangxing Lin;Yihong Zhuang;Yue Huang;Xinghao Ding;
      Pages: 1 - 14
      Abstract: Speckle suppression is a critical step in synthetic aperture radar (SAR) imaging. Since speckle-free SAR images are inaccessible, supervised denoising methods are not suitable for this task. To exploit the strong capabilities of convolutional neural networks (CNNs), we propose Unpaired Speckle Extraction (SAR-USE), an unsupervised method for SAR despeckling. Our method utilizes unpaired SAR and clean optical images to extract “real” speckle for learning despeckling. First, a CNN that has never seen clean SAR images is employed to extract speckle from the SAR image. Then, the extracted speckle is multiplied with a random optical image to synthesize paired data for learning speckle removal. Through a Siamese network, speckle extraction and learning despeckling are performed alternately and promote each other. To make the extracted speckle more visually and statistically realistic, it is constrained by a noise correction module to be unit mean while maintaining spatial correlation. After convergence, the CNN is a good denoiser that can effectively extract speckle from SAR images. Experiments on synthetic datasets show that the denoising ability of the proposed method is as good as its supervised counterpart. More importantly, SAR-USE is very efficient for removing the spatially correlated speckle in real data that supervised learning methods cannot.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Self-Supervised SAR Image Registration With SAR-Superpoint and
           Transformation Aggregation

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      Authors: Bin Zou;Haolin Li;Lamei Zhang;
      Pages: 1 - 15
      Abstract: Owing to various factors, including severe speckle noise and orbit direction differences, performing multitemporal synthetic aperture radar (SAR) image registration with high accuracy and robustness may become difficult. Herein, an efficient self-supervised deep learning registration network for multitemporal SAR image registration, SAR-superpoint and transformation aggregation network (SSTA-Net), is proposed. The SSTA-Net consists of three parts: 1) the SAR-Superpoint detection network (SS-Net); 2) the transformation aggregation feature matching network (TA-Net); and 3) the unstable point removal module. Specifically, a pseudolabel generation method is adopted without additional annotations. It transfers the characteristics of real SAR data to synthetic data through a feature transition module, which can generate feature point labels for real SAR images for self-training SS-Net. Furthermore, a position–channel aggregation attention is proposed and embedded into the SS-Net to efficiently capture position and channel information and to increase the stability and accuracy of feature point identification. Finally, a unique transformation aggregation strategy is designed to improve the robustness of feature matching, and an unstable point removal module is adopted to eliminate the mismatched point pairs caused by orbit differences. Six sets of multitemporal SAR images were used to evaluate the registration performance of the SSTA-Net, and our model was also compared with the traditional and deep learning algorithms. The experimental results demonstrate that the SSTA-Net outperforms various state-of-the-art approaches for SAR image registration.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Uncertainty-Incorporated Ice and Open Water Detection on Dual-Polarized
           SAR Sea Ice Imagery

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      Authors: Xinwei Chen;K. Andrea Scott;Linlin Xu;Mingzhe Jiang;Yuan Fang;David A. Clausi;
      Pages: 1 - 13
      Abstract: Algorithms designed for ice–water classification of synthetic aperture radar (SAR) sea ice imagery produce only binary (ice and water) output typically using manually labeled samples for assessment. This is limiting because only a small subset of labeled samples are used, which, given the nonstationary nature of the ice and water classes, will likely not reflect the full scene. To address this, we implement a binary ice–water classification in a more informative manner considering the uncertainty associated with each pixel in the scene. To accomplish this, we have implemented a Bayesian convolutional neural network (CNN) with variational inference to produce both aleatoric (data-based) and epistemic (model-based) uncertainty. This valuable information provides feedback as to regions that have pixels more likely to be misclassified and provides improved scene interpretation. Testing was performed on a set of 21 RADARSAT-2 dual-polarization SAR scenes covering a region in the Beaufort Sea captured regularly from April to December. The model is validated by demonstrating: 1) a positive correlation between misclassification rate and model uncertainty and 2) a higher uncertainty during the melt and freeze-up transition periods, which are more challenging to classify. By incorporating the iterative region growing with semantics (IRGS) segmentation algorithm and an uncertainty value-based thresholding algorithm, the Bayesian CNN classification outputs are improved significantly via both numerical analysis and visual inspection.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Cooperative SAR-Communication System Using Continuous Phase Modulation
           Codes and Mismatched Filters

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      Authors: Maria-Elisavet Chatzitheodoridi;Abigael Taylor;Olivier Rabaste;Hélène Oriot;
      Pages: 1 - 14
      Abstract: The electromagnetic congestion due to the continuous growth of spectral demand has been skyrocketing for the past years. Joint radar-communication systems are, thus, attracting attention as they can alleviate spectrum occupancy by using the same bandwidth to perform both applications. In this context, a cooperative radar-communication system, which is a specific category that uses communication codes to both transmit information and perform radar missions, can be considered. Continuous phase-modulated (CPM) codes are considered in this article in order to generate high-resolution radar images from airborne radar. Since mitigating the sidelobe level energy is essential for good image quality, we resort here to optimized mismatched filters (MMFs). A fast algorithm is proposed to minimize the computational time of these filters. Simulated data are generated, as well as resynthesized synthetic aperture radar (SAR) images, and reconstructed from real chirp-based data using CPM codes and MMFs. Their performance is evaluated using different comparison tools and shows that the use of mismatched filtering and different messages embedded in the phase of each transmitted code provides enhanced image quality.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • CZT Algorithm for the Doppler Scale Signal Model of Multireceiver SAS
           Based on Shear Theorem

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      Authors: Mengbo Ma;Jinsong Tang;Haoran Wu;Peng Zhang;Mingqiang Ning;
      Pages: 1 - 12
      Abstract: In multireceiver synthetic aperture sonars (SASs), the traditional non-stop-hop-stop model normally ignores the intrapulse motion of sonar platform. However, such intrapulse motion brings the Doppler scale effect (DSE) to the echo signal, which will lead to the change in carrier frequency (CF) and frequency modulation (FM) rate of echo signal and may eventually result in image defocus. In this article, considering the intrapulse motion, the time-varying time delay and range history of multireceiver SAS were derived by introducing the range time into the range history, and then the Doppler scale signal model was established. After approximating the derived time-varying range history, the 2-D spectrum was derived by using the shear theorem. When compared with the traditional 2-D spectrum, an interesting phenomenon was found that the change in FM rate has no effect on the 2-D spectrum. Actually, only two additional terms, i.e., range cell migration (ARCM) and azimuth modulation (AAM), were introduced into the new 2-D spectrum due to the change in CF. Besides, the condition of neglecting the ARCM was given by further analyzing the 2-D spectrum, while the AAM is usually small and can be easily compensated together with the ARCM correction. Finally, a chirp-z transform (CZT) algorithm for the Doppler scale signal model was proposed to correct the ARCM, which can simultaneously correct the ARCM and compensate the AAM (although not required) in the 2-D frequency domain by phase multiplication. The effectiveness of the signal model and the proposed algorithm is verified by simulations and field data.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Semisupervised SAR Ship Detection Network via Scene Characteristic
           Learning

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      Authors: Yuang Du;Lan Du;Yuchen Guo;Yu Shi;
      Pages: 1 - 17
      Abstract: In recent years, target detection methods based on deep learning have achieved extensive development in synthetic aperture radar (SAR) ship detection. However, training such detectors requires target-level annotations of SAR images that are hard to be obtained in practice. To reduce the dependence of network training on expensive target-level annotations, we propose a novel semisupervised SAR ship detection network via scene characteristic learning. The proposed network focuses on utilizing the scene-level annotations of SAR images to improve the detection performance in the case of limited target-level annotations. Compared with the traditional fully supervised SAR ship detection network, the proposed network constructs a scene characteristic learning branch parallel with the detection branch. In the scene characteristic learning branch, a scene classification loss and a scene aggregation loss are designed to utilize the scene-level annotations. Under the constraint of these two losses, the feature extraction network can fully learn the scene characteristics of SAR images, thus enhancing its feature representation ability for ship targets and clutter. In addition, we propose a hierarchical test process from scene to target. After recognizing the scene types of input SAR images, we design different detection strategies for SAR images recognized as different scenes. The proposed test process can significantly reduce the inland and inshore false alarms, thus leading to higher detection performance. The experiments based on two measured SAR ship detection datasets demonstrate the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Spatio-Temporal Filtering Approach for Tomographic SAR Data

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      Authors: Karima Hadj-Rabah;Gilda Schirinzi;Ishak Daoud;Faiza Hocine;Aichouche Belhadj-Aissa;
      Pages: 1 - 13
      Abstract: Synthetic aperture radar tomography (TomoSAR) has recently received particular interest from the remote-sensing community, due to its ability to provide 3-D reconstructions of environments with complex structures. Unfortunately, different forms of decorrelations and processing errors affect the quality of the resulting 2-D/3-D images. One way to cope with the impact of these nuisances is to apply appropriate filtering to the interferometric data stack as a preprocessing step. The first obstacle to be dealt with, especially in urban areas, is to define a filter whose parameters have to be set in such a way as to improve smoothing capabilities while preserving edges. To this aim, the main objective of this article is twofold: 1) the application of a spatio-temporal contextual filter whose parameters depend on 3-D quality indicators of the multibaseline interferometric image stack and 2) evaluation of the denoising effect on the application of nonparametric spectral estimation and detection algorithms. For that, we consider several quantitative metrics to assess, on the one hand, the filtering performances, and, on the other hand, its impact on the reflectivity function recovered from conventional tomographic inversion and detection methods. Experimental results from a set of TerraSAR-X (TSX) images highlight the efficiency of the filtering process by improving the scatterers’ detection and height localization, of a man-made structure.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A ConvLSTM Neural Network Model for Spatiotemporal Prediction of Mining
           Area Surface Deformation Based on SBAS-InSAR Monitoring Data

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      Authors: Sheng Yao;Yi He;Lifeng Zhang;Wang Yang;Yi Chen;Qiang Sun;Zhan’ao Zhao;Shengpeng Cao;
      Pages: 1 - 22
      Abstract: The surface deformation caused by underground mining leads to damage to surface buildings and brings potential safety hazards and property losses. The demand for reliable prediction methods of surface deformation in mining areas is becoming increasingly significant. At present, most prediction methods are based on sampling points; however, these methods neglect to consider local and overall spatial features, and this oversight affects the spatial accuracy of prediction results. The data form of the prediction output is often discontinuous and not intuitive. In order to solve this problem, the spatiotemporal prediction method of surface deformation in mining areas is a very effective proposal. However, few scholars have proposed a solution based on this idea. In this study, a convolution long short-term memory (ConvLSTM) neural network for surface deformation spatiotemporal prediction based on time-series interferometric synthetic aperture radar (InSAR) is proposed to directly predict the overall spatial deformation of the surface in the mining area. First, based on Sentinel-1A images of the Jinchuan Mining Area, Jinchang, Gansu province, China, the time-series InSAR surface deformation data of the study area from January 2018 to October 2020 (81 scenes) are obtained using small baseline subset InSAR (SBAS-InSAR) technology. Because of the large value scale of surface deformation in the mining area, we propose a method to fit the maximum and minimum values of time-series deformation, respectively, and carry out piecewise numerical compression. Then, based on the ConvLSTM neural network layer, construct the spatiotemporal prediction model of time-series InSAR surface deformation. Support vector regression (SVR), multilayer perceptron (MLP) regression, and the gray model [GM (1,1)] are used as benchmark methods. The prediction results of our models are compared with the three benchmark methods. The comparison results show that the prediction effect of the -onvLSTM model with optimal input time steps is significantly better than the benchmark methods in the comprehensive performance of various evaluation metrics, especially for the metrics used to evaluate the error. This shows that the ConvLSTM model has relatively fine spatiotemporal prediction performance for time-series InSAR surface deformation. Based on the InSAR time-series deformation monitoring results of the Jinchuan Mining Area, we carry out the spatiotemporal prediction of surface deformation in the subsequent 50 time steps (600 days). The results agree with the natural deformation development law in band collection statistics and 3-D representation of deformation; moreover, the reliability of numerical spatial distribution is relatively high. This result can be used to intuitively evaluate the overall surface deformation of the mining area in the monitoring range, find potential hazards in time, and take measures to address these hazards quickly. At the same time, this research process also provides a new concept design for such problems.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Study of Hypersonic Vehicle-Borne SAR Imaging Under Plasma Sheath

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      Authors: Lihao Song;Bowen Bai;Xiaoping Li;Gezhao Niu;Yanming Liu;Yiding Mao;
      Pages: 1 - 13
      Abstract: Hypersonic vehicle-borne synthetic aperture radar (SAR) imaging has promising application prospects for remote sensing owing to the characteristics of fast response and flexible trajectory. However, a newly observed phenomenon that occurs during hypersonic flight, called the plasma sheath, seriously affects hypersonic vehicle-borne SAR imaging. Therefore, in this study, the effect of plasma sheath on hypersonic vehicle-borne SAR imaging is investigated systematically. First, a hypersonic vehicle-borne SAR signal model under plasma sheath is developed. Then, the phase shift and amplitude attenuation which are critical in the SAR signal model under plasma sheath are deduced. Moreover, the analytical formulas for the linear and quadratic phase errors are derived. Based on the developed signal model, point target response and an SAR image of an area under plasma sheath show that linear phase error will cause image shift, and quadratic phase error will lead to pulse broadening. In addition, the amplitude attenuation will make the point target response submerged in noise, and signal amplitude nonlinear distortion will result in an asymmetric distortion phenomenon. To further explore the effect of plasma sheath on hypersonic vehicle-borne SAR imaging, quantitative evaluation and analysis of SAR imaging degradation are conducted under different electron densities, carrier frequencies, and bandwidths. The evaluation results in this study will benefit the application of hypersonic vehicle-borne SAR imaging under plasma sheath, and the analytical formulas of linear and quadratic phase error provide the possibility to compensate for SAR imaging degradation under plasma sheath in the future.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Permittivity Extraction of Soil Samples Using Coaxial-Line Measurements by
           a Simple Calibration

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      Authors: Hafize Hasar;Ugur C. Hasar;Hamdullah Ozturk;Mucahit Izginli;Nevzat Aslan;Taskin Oztas;Mehmet Ertugrul;Mehmet Karayilan;Omar M. Ramahi;
      Pages: 1 - 8
      Abstract: A new microwave method is proposed for accurate determination of complex permittivity $varepsilon _{textrm {rs}} = varepsilon _{textrm {rs}}^{prime }--j varepsilon _{textrm {rs}}^{prime prime }$ of soil samples inserted over a holder within the Electronic Industries Association (EIA) 1-5/8” coaxial measurement cell. Such a determination could be indirectly correlated with the volumetric moisture content of soil samples by microwave measurements. The method has three main advantages. First, it utilizes a simple calibration procedure involving uncalibrated measurements of an empty cell, the same cell loaded with a soil holder (a dielectric sample), and the same cell with a soil sample over this holder, thus eliminating the need for any formal calibration procedure. Second, it uses one measurement cell for extracting $varepsilon _{textrm {rs}}$ . Third, it does not require any numerical toolbox for determining $varepsilon _{textrm {rs}}$ . The method is validated by simulations of a synthesized soil sample and by experiments with a low-loss polyethylene sample. Its accuracy is examined in reference to: 1) two measurement cells with different lengths (length independence); 2) the position of the holder in the cell; and 3) an offset in sample length. Calibration curves (the volumetric moisture content $theta _{V}$ versus $varepsilon _{textrm {rs}}^{prime }$ ) obtained from fitting measured $varepsilon _{textrm {rs}}^{prime }$ by our method to $theta _{V}$ at 2 and 3 GHz are compared with other calibration curves in the literature for the analysis of the performance of the proposed method (PM). It is shown that calibration curves obtained from our method are similar to those obtained from other methods requiring complex calibration procedures.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Blind Superresolution of Satellite Videos by Ghost Module-Based
           Convolutional Networks

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      Authors: Zhi He;Dan He;Xiaofang Li;Rongning Qu;
      Pages: 1 - 19
      Abstract: Deep learning (DL)-based video satellite superresolution (SR) methods have recently yielded superior performance over traditional model-based methods by using an end-to-end manner. Existing DL-based methods usually assume that the blur kernels are known and, thus, do not model the blur kernels during restoration. However, this assumption is rarely held for real satellite videos and leads to oversmoothed results. In this article, we propose a Ghost module-based convolution network model for blind SR of satellite videos. The proposed Ghost module-based video SR (GVSR) method, which assumes that the blur kernel is unknown, consists of two main modules, i.e., the preliminary image generation module and the SR results’ reconstruction module. First, the motion information from adjacent video frames and the wrapped images are explored by an optical flow estimation network, the blur kernel is flexibly obtained by a blur kernel estimation network, and the preliminary high-resolution image is generated by feeding both blur kernel and wrapped images. Second, a reconstruction network consisting of three paths with attention-based Ghost (AG) bottlenecks is designed to remove artifacts in the preliminary image and obtain the final high-quality SR results. Experiments conducted on Jilin-1 and OVS-1 satellite videos demonstrate that the qualitative and quantitative performance of our proposed method is superior to current state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Spectral Super-Resolution Based on Dictionary Optimization Learning via
           Spectral Library

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      Authors: Hao-Fang Yan;Yong-Qiang Zhao;Jonathan Cheung-Wai Chan;Seong G Kong;
      Pages: 1 - 16
      Abstract: Extensive works have been reported in hyperspectral images (HSIs) and multispectral images (MSIs) fusion to raise the spatial resolution of HSIs. However, the limited acquisition of HSIs has been an obstacle to such approaches. Spectral super-resolution (SSR) of MSI is a challenging and less investigated topic, which can also provide high-resolution synthetic HSIs. To deal with this high ill-posedness problem, we perform super-resolution enhancement of MSIs in the spectral domain by incorporating a spectral library as a priori. First, an aligned spectral library, which maps the open-source spectral library to a specific spectral library created for the reconstructed HR HSI, is represented. An intermediate latent HSI is obtained by fusing the spatial information from MSI and the hyperspectral information from a specific spectral library. Then, we use low-rank attribute embedding to transfer latent HSI into a robust subspace. Finally, a low-rank HSI dictionary representing the hyperspectral information is learned from the latent HSI. The adaptive sparse coefficient of MSI is obtained with a nonnegative constraint. By fusing these two terms, we get the final HR HSI. The proposed SSR model does not require any pretraining stages. We confirm the validity and superiority of our proposed SSR algorithm by comparing it with several benchmark state-of-the-art approaches on different datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Modality-Aware Feature Integration for Pan-Sharpening

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      Authors: Man Zhou;Jie Huang;Feng Zhao;Danfeng Hong;
      Pages: 1 - 12
      Abstract: Pan-sharpening aims to super-solve low-spatial resolution multiple spectral (MS) images with the guidance of high-resolution (HR) texture-rich panchromatic (PAN) images. Recently, deep-learning-based pan-sharpening approaches have dominated this field and achieved remarkable advancement. However, most promising algorithms are devised in one-way mapping and have not fully explored the mutual dependencies between PAN and MS modalities, thus impacting the model performance. To address this issue, we propose a novel information compensation and integration network for pan-sharpening by effective cross-modality joint learning in this work. First, the cross-central difference convolution is employed to explicitly extract the texture details of the PAN images. Second, we implement the compensation process by imitating the classical back-projection (BP) technique where the extracted PAN textures are employed to guide the intrinsic information learning of MS images iteratively. Subsequently, we devise the hierarchical transformer to integrate the comprehensive relations of stage-iteration information from spatial and temporal contexts. Extensive experiments over multiple satellite datasets demonstrate the superiority of our method to the existing state-of-the-art methods. The source code is available at https://github.com/manman1995/pansharpening.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Sal²RN: A Spatial–Spectral Salient Reinforcement Network for
           Hyperspectral and LiDAR Data Fusion Classification

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      Authors: Jiaojiao Li;Yuzhe Liu;Rui Song;Yunsong Li;Kailiang Han;Qian Du;
      Pages: 1 - 14
      Abstract: Hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion have been widely employed in HSI classification to promote interpreting performance. In the existing deep learning methods based on spatial–spectral features, the features extracted from different layers are treated fairly in the learning process. In reality, features extracted from the continuous layers contribute differentially to the final classification, such as large tracts of woodland and agriculture typically count on shallow contour features, whereas deep semantic spectral features have meaningful constraints for small entities like vehicles. Furthermore, the majority of existing classification algorithms employ a patch input scheme, which has a high probability to introduce pixels of different categories at the boundary. To acquire more accurate classification results, we propose a spatial–spectral saliency reinforcement network (Sal2RN) in this article. In spatial dimension, a novel cross-layer interaction module (CIM) is presented to adaptively alter the significance of features between various layers and integrate these diversified features. Moreover, a customized center spectrum correction module (CSCM) integrates neighborhood information and adaptively modifies the center spectrum to reduce intraclass variance and further improve the classification accuracy of the network. Finally, a statistically based feature weighted combination module is constructed to effectively fuse spatial, spectral, and LiDAR features. Compared with traditional and advanced classification methods, the Sal2RN achieves the state-of-the-art classification performance on three open benchmark datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Implicit Neural Representation Learning for Hyperspectral Image
           Super-Resolution

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      Authors: Kaiwei Zhang;Dandan Zhu;Xiongkuo Min;Guangtao Zhai;
      Pages: 1 - 12
      Abstract: Hyperspectral image (HSI) super-resolution (SR) without additional auxiliary image remains a constant challenge due to its high-dimensional spectral patterns, where learning an effective spatial and spectral representation is a fundamental issue. Recently, implicit neural representations (INRs) are making strides as a novel and effective representation, especially in the reconstruction task. Therefore, in this work, we propose a novel HSI reconstruction model based on INR which represents HSI by a continuous function mapping a spatial coordinate to its corresponding spectral radiance values. In particular, as a specific implementation of INR, the parameters of the parametric model are predicted by a hypernetwork that operates on feature extraction using a convolution network. It makes the continuous functions map the spatial coordinates to pixel values in a content-aware manner. Moreover, periodic spatial encoding is deeply integrated with the reconstruction procedure, which makes our model capable of recovering more high-frequency details. To verify the efficacy of our model, we conduct experiments on three HSI datasets (CAVE, NUS, and NTIRE2018). Experimental results show that the proposed model can achieve competitive reconstruction performance in comparison with the state-of-the-art methods. In addition, we provide an ablation study on the effect of individual components of our model. We hope this article could serve as a potent reference for future research.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • MR-Selection: A Meta-Reinforcement Learning Approach for Zero-Shot
           Hyperspectral Band Selection

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      Authors: Jie Feng;Gaiqin Bai;Di Li;Xiangrong Zhang;Ronghua Shang;Licheng Jiao;
      Pages: 1 - 20
      Abstract: Band selection is an effective method to deal with the difficulties in image transmission, storage, and processing caused by redundant and noisy bands in hyperspectral images (HSIs). Existing band selection methods usually need to learn a specific model for each HSI dataset, which ignores the inherent correlation and common knowledge among different band selection tasks. Meanwhile, these methods lead to a huge waste of computation. In this article, a novel zero-shot band selection method, called MR-Selection, is proposed for HSI classification. It formalizes zero-shot band selection as a metalearning problem, where advantage actor–critic algorithm-based reinforcement learning (A2C-RL) is designed to extract the metaknowledge in the band selection tasks of various seen hyperspectral datasets through a shared agent. To learn a consistent representation among different tasks, a dynamic structure-aware graph convolutional network is constructed to build a shared agent in A2C-RL. In A2C-RL, the state is tailored in a feasible way and easy to adapt to various tasks. Meanwhile, the reward is defined according to an efficient evaluation network, which can evaluate each state effectively without any fine-tuning. Furthermore, a two-stage optimization strategy is designed to coordinate optimization directions of a shared agent from different tasks effectively. Once the shared agent is optimized, it can be directly applied to unseen HSI band selection tasks without any available samples. Experimental results demonstrate the effectiveness and efficiency of the MR-Selection on the band selection of unseen HSI datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Hyperspectral Sparse Unmixing via Nonconvex Shrinkage Penalties

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      Authors: Longfei Ren;Danfeng Hong;Lianru Gao;Xu Sun;Min Huang;Jocelyn Chanussot;
      Pages: 1 - 15
      Abstract: Hyperspectral sparse unmixing aims at finding the optimal subset of spectral signatures in the given spectral library and estimating their proportions in each pixel. Recently, simultaneously sparse and low-rank representations (SSLRRs) have been widely used in the hyperspectral sparse unmixing task. This article developed a new unified framework to approximate the SSLRR-based unmixing model. The heart of the proposed framework is to design the new nonconvex penalties for efficient minimization by the means of two families of thresholding mappings, including the firm thresholding mapping and generalized shrinkage mapping. Both mappings can be regarded as generalizations of both soft thresholding and hard thresholding. Unlike previous approaches with explicit penalties, the proposed framework does not require explicit forms of penalties but only relevant thresholding mappings. Furthermore, an alternating direction method of multipliers (ADMM) was designed to solve the resulting optimization problem. Experiments conducted on the synthetic data and real data demonstrate the superiority of the proposed framework in improving the unmixing performance with respect to state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Hyperspectral Remote Sensing Image Synthesis Based on Implicit Neural
           Spectral Mixing Models

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      Authors: Liqin Liu;Zhengxia Zou;Zhenwei Shi;
      Pages: 1 - 14
      Abstract: Hyperspectral image (HSI) synthesis, as an emerging research topic, is of great value in overcoming sensor limitations and achieving low-cost acquisition of high-resolution remote sensing HSIs. However, the linear spectral mixing model used in recent studies oversimplifies the real-world hyperspectral imaging process, making it difficult to effectively model the imaging noise and multiple reflections of the object spectrum. As a prerequisite for hyperspectral data synthesis, accurate modeling of nonlinear spectral mixtures has long been a challenge. Considering the above difficulties, we propose a novel method for modeling nonlinear spectral mixtures based on implicit neural representations (INRs) in this article. The proposed method learns from INR and adaptively implements different mixture models for each pixel according to their spectral signature and surrounding environment. Based on the above neural mixing model, we also propose a new method for HSI synthesis. Given an RGB image as input, our method can generate an accurate and physically meaningful HSI. As a set of by-products, our method can also generate subpixel-level spectral abundance as well as the solar atmosphere signature. The whole framework is trained end-to-end in a self-supervised manner. We constructed a new dataset for HSI synthesis based on a wide range of Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Our method achieves a mean peak signal-to-noise ratio (MPSNR) of 52.36 dB and outperforms other state-of-the-art hyperspectral synthesis methods. Finally, our method shows great benefits to downstream data-driven applications. With the HSIs and abundance directly generated from low-cost RGB images, the proposed method improves the accuracy of HSI classification tasks by a large margin, particularly for those with limited training samples.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Bayesian Meta-Learning-Based Method for Few-Shot Hyperspectral Image
           Classification

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      Authors: Jing Zhang;Liqin Liu;Rui Zhao;Zhenwei Shi;
      Pages: 1 - 13
      Abstract: Few-shot learning provides a new way to solve the problem of insufficient training samples in hyperspectral classification. It can implement reliable classification under several training samples by learning meta-knowledge from similar tasks. However, most existing works perform frequency statistics, which may suffer from the prevalent uncertainty in point estimates (PEs) with limited training samples. To overcome this problem, we reconsider the hyperspectral image few-shot classification (HSI-FSC) task as a hierarchical probabilistic inference from a Bayesian view and provide a careful process of meta-learning probabilistic inference. We introduce a prototype vector for each class as latent variables and adopt distribution estimates (DEs) for them to obtain their posterior distribution. The posterior of the prototype vectors is maximized by updating the parameters in the model via the prior distribution of HSI and labeled samples. The features of the query samples are matched with prototype vectors drawn from the posterior; thus, a posterior predictive distribution over the labels of query samples can be inferred via an amortized Bayesian variational inference approach. Experimental results on four datasets demonstrate the effectiveness of our method. Especially given only three to five labeled samples, the method achieves noticeable upgrades of overall accuracy (OA) against competitive methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Joint Classification of Hyperspectral and LiDAR Data Using a Hierarchical
           CNN and Transformer

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      Authors: Guangrui Zhao;Qiaolin Ye;Le Sun;Zebin Wu;Chengsheng Pan;Byeungwoo Jeon;
      Pages: 1 - 16
      Abstract: The joint use of multisource remote-sensing (RS) data for Earth observation missions has drawn much attention. Although the fusion of several data sources can improve the accuracy of land-cover identification, many technical obstacles, such as disparate data structures, irrelevant physical characteristics, and a lack of training data, exist. In this article, a novel dual-branch method, consisting of a hierarchical convolutional neural network (CNN) and a transformer network, is proposed for fusing multisource heterogeneous information and improving joint classification performance. First, by combining the CNN with a transformer, the proposed dual-branch network can significantly capture and learn spectral–spatial features from hyperspectral image (HSI) data and elevation features from light detection and ranging (LiDAR) data. Then, to fuse these two sets of data features, a cross-token attention (CTA) fusion encoder is designed in a specialty. The well-designed deep hierarchical architecture takes full advantage of the powerful spatial context information extraction ability of the CNN and the strong long-range dependency modeling ability of the transformer network based on the self-attention (SA) mechanism. Four standard datasets are used in experiments to verify the effectiveness of the approach. The experimental results reveal that the proposed framework can perform noticeably better than state-of-the-art methods. The source code of the proposed method will be available publicly at https://github.com/zgr6010/Fusion_HCT.git.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Edge-Guided Hyperspectral Image Compression With Interactive Dual
           Attention

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      Authors: Yuanyuan Guo;Yulong Tao;Yanwen Chong;Shaoming Pan;Miao Liu;
      Pages: 1 - 17
      Abstract: Compressing hyperspectral images (HSIs) into compact representations under the premise of ensuring high-quality reconstruction is an essential task in HSI processing. However, existing compression methods usually encode images by smoothing due to the low-frequency information occupying a prominent component in most images. Consequently, these methods fail to capture sufficient structural information, especially in low bit rates, often causing inferior reconstruction. To address this problem, we propose here an edge-guided hyperspectral compression network, called CENet, to realize high-quality reconstruction. To enhance the structural latent representation ability, the CENet model incorporates an edge extractor neural network into the compression architecture to guide compression optimization by the edge-guided loss. We propose an interactive dual attention module to selectively learn edge features, obtain the most effective edge structure, and avoid additional edge information redundancy at the same time. In the proposed CENet, the edge-guided loss and interactive dual attention module are combined to enhance the comprehensive structure of the latent representation. Concretely, interactive dual attention makes the edge extraction network focus only on moderate boundaries rather than on all edges, which enables savings on the bit rate cost and helps achieve a strong structural representation. As a result, the reconstruction quality is significantly improved. The extensive experiments on seven HSI datasets verify that our model can effectively raise the rate–distortion performance for HSIs of any type or resolution (e.g., yielding an average peak signal-to-noise ratio (PSNR) of 30.59 dB at 0.2382 bpppb, which exceeds the baseline for Chikusei by 10.99%).
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Novel Hyperspectral Image Classification Model Using Bole Convolution
           With Three-Direction Attention Mechanism: Small Sample and Unbalanced
           Learning

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      Authors: Weiwei Cai;Xin Ning;Guoxiong Zhou;Xiao Bai;Yizhang Jiang;Wei Li;Pengjiang Qian;
      Pages: 1 - 17
      Abstract: Currently, the use of rich spectral and spatial information of hyperspectral images (HSIs) to classify ground objects is a research hotspot. However, the classification ability of existing models is significantly affected by its high data dimensionality and massive information redundancy. Therefore, we focus on the elimination of redundant information and the mining of promising features and propose a novel Bole convolution (BC) neural network with a tandem three-direction attention (TDA) mechanism (BTA-Net) for the classification of HSI. A new BC is proposed for the first time in this algorithm, whose core idea is to enhance effective features and eliminate redundant features through feature punishment and reward strategies. Considering that traditional attention mechanisms often assign weights in a one-direction manner, leading to a loss of the relationship between the spectra, a novel three-direction (horizontal, vertical, and spatial directions) attention mechanism is proposed, and an addition strategy and a maximization strategy are used to jointly assign weights to improve the context sensitivity of spatial–spectral features. In addition, we also designed a tandem TDA mechanism module and combined it with a multiscale BC output to improve classification accuracy and stability even when training samples are small and unbalanced. We conducted scene classification experiments on four commonly used hyperspectral datasets to demonstrate the superiority of the proposed model. The proposed algorithm achieves competitive performance on small samples and unbalanced data, according to the results of comparison and ablation experiments. The source code for BTA-Net can be found at https://github.com/vivitsai/BTA-Net.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • t-Linear Tensor Subspace Learning for Robust Feature Extraction of
           Hyperspectral Images

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      Authors: Yang-Jun Deng;Heng-Chao Li;Si-Qiao Tan;Junhui Hou;Qian Du;Antonio Plaza;
      Pages: 1 - 15
      Abstract: Subspace learning has been widely applied for feature extraction of hyperspectral images (HSIs) and achieved great success. However, the current methods still leave two problems that need to be further investigated. First, those methods mainly focus on finding one or multiple projection matrices for mapping the high-dimensional data into a low-dimensional subspace, which can only capture the information from each direction of high-order hyperspectral data separately. Second, the performance of feature extraction is barely satisfactory when the hyperspectral data is severely corrupted by noise. To address these issues, this article presents a t-linear tensor subspace learning (tLTSL) model for robust feature extraction of HSIs based on t-product projection. In the model, t-product projection is a new defined tensor transformation way similar to linear transformation in vector space, which can maximally capture the intrinsic structure of tensor data. The integrated tensor low-rank and sparse decomposition can effectively remove the noise corruption and the learned t-product projection can directly transform the high-order hyperspectral data into a subspace with information from all modes comprehensively considered. Moreover, a proposition related to tensor rank is proofed for interpreting the meaning of the tLTSL model. Extensive experiments are conducted on two different kinds of noise (i.e., simulated and real noise) corrupted HSI data, which validate the effectiveness of tLTSL.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Graph Meta Transfer Network for Heterogeneous Few-Shot Hyperspectral Image
           Classification

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      Authors: Haoyu Wang;Xuesong Wang;Yuhu Cheng;
      Pages: 1 - 12
      Abstract: Since obtaining labeled hyperspectral images (HSIs) is difficult and time-consuming, the shortage of training samples has always been a challenge for HSI classification. In practical applications, only a few labeled samples are available in the task domain (target domain), while sufficient labeled samples are available in another domain (source domain). At the same time, these two domains are heterogeneous and contain different categories. This scenario makes it difficult to effectively transfer knowledge from the source domain to the target domain. To address this challenge, we propose a novel heterogeneous few-shot learning (FSL) method, namely graph meta transfer network (GMTN). Specifically, the graph sample and aggregate network (GraphSAGE) and meta-learning, which are both inductive learning, are integrated into a unified framework. In this way, the aggregation function is generalized from abundant few-shot tasks for feature extraction on the source and target domains. The spatial importance strategy (SIS) is designed to guide the feature propagation and alleviate the information interference caused by different categories. The neighborhood receptive field spectral attention (RFSA) mechanism is proposed to model the importance of spectral band using the information of the neighborhood pixels, which enables GMTN to pay more attention to bands with discriminative features in both domains. In addition, the node spatial information reset method is proposed to augment samples based on the spatial position relationship of nodes. Furthermore, to alleviate the domain shift in heterogeneous scenarios, the conditional domain adversarial strategy is used to achieve effective meta-knowledge transfer. Experiments show that GMTN outperforms the compared state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Morphological Transformation and Spatial-Logical Aggregation for Tree
           Species Classification Using Hyperspectral Imagery

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      Authors: Mengmeng Zhang;Wei Li;Xudong Zhao;Huan Liu;Ran Tao;Qian Du;
      Pages: 1 - 12
      Abstract: Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which contribute to a more accurate identification of materials and land covers. However, most existing methods of hyperspectral image analysis primarily focus on spectral knowledge or coarse-grained spatial information while neglecting the fine-grained morphological structures. In the classification task of complex objects, spatial morphological differences can help to search for the boundary of fine-grained classes, e.g., forestry tree species. Focusing on subtle traits extraction, a spatial-logical aggregation network (SLA-NET) is proposed with morphological transformation for tree species classification. The morphological operators are effectively embedded with the trainable structuring elements, which contributes to distinctive morphological representations. We evaluate the classification performance of the proposed method on two tree species datasets, and the results demonstrate that the proposed SLA-NET significantly outperforms the other state-of-the-art classifiers.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Language-Aware Domain Generalization Network for Cross-Scene Hyperspectral
           Image Classification

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      Authors: Yuxiang Zhang;Mengmeng Zhang;Wei Li;Shuai Wang;Ran Tao;
      Pages: 1 - 12
      Abstract: Text information including extensive prior knowledge about land cover classes has been ignored in hyperspectral image (HSI) classification tasks. It is necessary to explore the effectiveness of linguistic mode in assisting HSI classification. In addition, the large-scale pretraining image–text foundation models have demonstrated great performance in a variety of downstream applications, including zero-shot transfer. However, most domain generalization methods have never addressed mining linguistic modal knowledge to improve the generalization performance of model. To compensate for the inadequacies listed above, a language-aware domain generalization network (LDGnet) is proposed to learn cross-domain-invariant representation from cross-domain shared prior knowledge. The proposed method only trains on the source domain (SD) and then transfers the model to the target domain (TD). The dual-stream architecture including the image encoder and text encoder is used to extract visual and linguistic features, in which coarse-grained and fine-grained text representations are designed to extract two levels of linguistic features. Furthermore, linguistic features are used as cross-domain shared semantic space, and visual–linguistic alignment is completed by supervised contrastive learning in semantic space. Extensive experiments on three datasets demonstrate the superiority of the proposed method when compared with the state-of-the-art techniques. The codes will be available from the website: https://github.com/YuxiangZhang-BIT/IEEE_TGRS_LDGnet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Lightweight Multiresolution Feature Fusion Network for Spectral
           Super-Resolution

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      Authors: Shaohui Mei;Ge Zhang;Nan Wang;Bo Wu;Mingyang Ma;Yifan Zhang;Yan Feng;
      Pages: 1 - 14
      Abstract: Spectral super-resolution (SR), which reconstructs high spatial-resolution hyperspectral images (HSIs) from RGB inputs, has been demonstrated to be one of the effective computational imaging techniques to acquire HSIs. Though deep neural networks have shown their superiority in such a complex mapping problem, existing networks generally involve a very complex structure with huge amounts of parameters, resulting in giant memory occupation. In this article, a lightweight multiresolution feature fusion network (MRFN) is proposed, which adopts a multiresolution feature extraction and fusion framework to fully explore RGB inputs in different scales of resolution. Specifically, a lightweight feature extraction module (LFEM), which adopts cheap convolution and attention mechanisms, is constructed to explore different scales of features under a lightweight structure. Moreover, a hybrid loss function is proposed by encountering not only pixel-value level reconstruction error but also spectral continuity and fidelity. Experiments over three benchmark datasets, i.e., CAVE, Interdisciplinary Computational Vision Laboratory (ICVL), and NTIRE2022 datasets, have demonstrated that the proposed MRFN can reconstruct HSIs from RGB inputs in higher quality with fewer parameters and computational floating-point operations (FLOPs) compared with several state-of-the-art networks.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Abundance Matrix Correlation Analysis Network Based on Hierarchical
           Multihead Self-Cross-Hybrid Attention for Hyperspectral Change Detection

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      Authors: Wenqian Dong;Jingyu Zhao;Jiahui Qu;Song Xiao;Nan Li;Shaoxiong Hou;Yunsong Li;
      Pages: 1 - 13
      Abstract: Hyperspectral image (HSI) change detection is a technique for detecting the changes between the multitemporal HSIs of the same scene. Many existing change detection methods have achieved good results, but there still exist problems as follows: 1) mixed pixels exist in HSI due to the low spatial resolution of hyperspectral sensor and other external interference and 2) many existing deep learning-based networks cannot make full use of the correlation difference information between the bitemporal images. These problems are not conducive to further improving the accuracy of change detection. In this article, we propose an abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention (AMCAN-HMSchA) for HSI change detection, which hierarchically highlights the correlation difference information at the subpixel level to detect the subtle changes. The endmember sharing-based abundance matrix learning module (AMLM) maps the changed information between bitemporal HSIs to the corresponding abundance matrices. The hierarchical MSchA extracts the enhanced difference features by constantly comparing the self-correlation with cross correlation between the abundance matrices of the HSIs. Then, the difference features are concatenated and fed into the fully connected layers to obtain the change map. Experiments on three widely used datasets show that the proposed method has superior performance compared with other state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Multiscale and Cross-Level Attention Learning for Hyperspectral Image
           Classification

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      Authors: Fulin Xu;Ge Zhang;Chao Song;Hui Wang;Shaohui Mei;
      Pages: 1 - 15
      Abstract: Transformer-based networks, which can well model the global characteristics of inputted data using the attention mechanism, have been widely applied to hyperspectral image (HSI) classification and achieved promising results. However, the existing networks fail to explore complex local land cover structures in different scales of shapes in hyperspectral remote sensing images. Therefore, a novel network named multiscale and cross-level attention learning (MCAL) network is proposed to fully explore both the global and local multiscale features of pixels for classification. To encounter local spatial context of pixels in the transformer, a multiscale feature extraction (MSFE) module is constructed and implemented into the transformer-based networks. Moreover, a cross-level feature fusion (CLFF) module is proposed to adaptively fuse features from the hierarchical structure of MSFEs using the attention mechanism. Finally, the spectral attention module (SAM) is implemented prior to the hierarchical structure of MSFEs, by which both the spatial context and spectral information are jointly emphasized for hyperspectral classification. Experiments over several benchmark datasets demonstrate that the proposed MCAL obviously outperforms both the convolutional neural network (CNN)-based and transformer-based state-of-the-art networks for hyperspectral classification.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Orthogonal Subspace Unmixing to Address Spectral Variability for
           Hyperspectral Image

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      Authors: Longfei Ren;Danfeng Hong;Lianru Gao;Xu Sun;Min Huang;Jocelyn Chanussot;
      Pages: 1 - 13
      Abstract: Hyperspectral unmixing aims at estimating pure spectral signatures and their proportions in each pixel. In practice, the atmospheric effects, intrinsic variation of the spectral signatures of the materials, illumination, and topographic changes cause what is known as spectral variability resulting in significant estimation errors being propagated throughout the unmixing task. To this end, we developed a new method, called the orthogonal subspace unmixing (OSU), to address spectral variability by utilizing the orthogonal subspace projection. The proposed OSU method jointly performs orthogonal subspace learning and the unmixing process to find a more suitable subspace for unmixing. The orthogonal subspace projection encourages the representation held in the subspace to be more distinct from each other to remove the complex spectral variability in the subspace. Furthermore, an alternating minimization (AM) was designed to solve the resulting optimization problem. An efficient and convergent symmetric Gauss–Seidel alternating direction method of multipliers (sGS-ADMM), essentially a special case of the semiproximal alternating direction method of multipliers (SPADMM), was developed to solve the subproblem. Experiments conducted on one synthetic data and two real data demonstrate the effectiveness and superiority of the proposed framework in mitigating the effects of spectral variability with respect to classical linear unmixing methods or variability accounting approaches.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR
           Data Classification

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      Authors: Meng Wang;Feng Gao;Junyu Dong;Heng-Chao Li;Qian Du;
      Pages: 1 - 16
      Abstract: The joint hyperspectral image (HSI) and light detection and ranging (LiDAR) data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in the existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a nearest neighbor-based contrastive learning network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://github.com/summitgao/NNCNet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • On Improving Bounding Box Representations for Oriented Object Detection

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      Authors: Yanqing Yao;Gong Cheng;Guangxing Wang;Shengyang Li;Peicheng Zhou;Xingxing Xie;Junwei Han;
      Pages: 1 - 11
      Abstract: Detecting objects in remote sensing images (RSIs) using oriented bounding boxes (OBBs) is flourishing but challenging, wherein the design of OBB representations is the key to achieving accurate detection. In this article, we focus on two issues that hinder the performance of the two-stage oriented detectors: 1) the notorious boundary discontinuity problem, which would result in significant loss increases in boundary conditions, and 2) the inconsistency in regression schemes between the two stages. We propose a simple and effective bounding box representation by drawing inspiration from the polar coordinate system and integrate it into two detection stages to circumvent the two issues. The first stage specifically initializes four quadrant points as the starting points of the regression for producing high-quality oriented candidates without any postprocessing. In the second stage, the final localization results are refined using the proposed novel bounding box representation, which can fully release the capabilities of the oriented detectors. Such consistency brings a good trade-off between accuracy and speed. With only flipping augmentation and single-scale training and testing, our approach with ResNet-50-FPN harvests 76.25% mAP on the DOTA dataset with a speed of up to 16.5 frames/s, achieving the best accuracy and the fastest speed among the mainstream two-stage oriented detectors. Additional results on the DIOR-R and HRSC2016 datasets also demonstrate the effectiveness and robustness of our method. The source code is publicly available at https://github.com/yanqingyao1994/QPDet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • DASRSNet: Multitask Domain Adaptation for Super-Resolution-Aided Semantic
           Segmentation of Remote Sensing Images

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      Authors: Yuxiang Cai;Yingchun Yang;Yongheng Shang;Zhengwei Shen;Jianwei Yin;
      Pages: 1 - 18
      Abstract: Unsupervised domain adaptation (UDA) has become an important technique for cross-domain semantic segmentation (SS) in the remote sensing community and obtained remarkable results. However, when transferring from high-resolution (HR) remote sensing images to low-resolution (LR) images, the existing UDA frameworks always fail to segment the LR target images, especially for small objects (e.g., cars), due to the severe spatial resolution shift problem. In this article, to improve the segmentation ability of UDA models for LR target images and small objects, we propose a novel multitask domain adaptation network (DASRSNet) for SS of remote sensing images with the aid of super-resolution (SR). The proposed DASRSNet contains domain adaptation for SS (DASS) branch, domain adaptation for SR (DASR) branch, and feature affinity (FA) module. Specifically, the DASS and DASR branches share the same encoder to extract the domain-invariant features for the target and source domains, and these two branches utilize different decoders and discriminators to conduct cross-domain SS task and SR task, which align the domain shift in output space and image space, respectively. Finally, the FA module, which involves the proposed FA loss, is applied to enhance the affinity of SS features and SR features for both source and target domains. The experimental results on the cross-city aerial datasets demonstrate the effectiveness and superiority of our DASRSNet against the recent UDA models.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • PCLUDA: A Pseudo-Label Consistency Learning- Based Unsupervised Domain
           

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      Authors: Dongyang Hou;Siyuan Wang;Xueqing Tian;Huaqiao Xing;
      Pages: 1 - 14
      Abstract: Recent advances in deep learning have dramatically improved the performance of content-based remote sensing image retrieval (CBRSIR) with the same distribution of training set (source domain) and test set (target domain). In fact, their distributions are inconsistent in most cases, which can lead to a dramatic decrease in retrieval performance. Currently, some unsupervised domain adaptation (DA) methods for other remote sensing applications have been proposed to eliminate the inconsistency. However, the current unsupervised DA methods do not make full use of the target domain’s distribution characteristics when delineating its decision boundary. This tends to degrade the cross-domain retrieval performance. In this article, a pseudo-label consistency learning-based unsupervised DA method (PCLUDA) is proposed for cross-domain CBRSIR. Our PCLUDA method minimizes the difference in probability distribution between the target domain and its perturbed output by a pseudo-label self-training and consistency regularization strategy, followed by adjusting the target domain’s decision boundaries to the low-density region. Besides, minimize class confusion (MCC) is introduced to reduce negative transfer caused by large intraclass variance of RSIs. Two cross-domain datasets with 12 cross-domain scenarios are constructed based on six open access datasets to measure DA methods. Experimental results show that our PCLUDA method achieves superior retrieval performances with average retrieval precision improvement by 4.9%–32.3% compared with eight state-of-the-art DA approaches in complex cross-domain scenarios. Furthermore, other experimental results indicate that our PCLUDA can also reach optimal retrieval performances in different kinds of deep learning networks [i.e., vision transformer (ViT) and convolutional neural networks (CNNs)].
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Triple-Stream Network With Cross-Stage Feature Fusion for
           High-Resolution Image Change Detection

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      Authors: Yu Zhao;Pan Chen;Zhengchao Chen;Yongqing Bai;Zhujun Zhao;Xuan Yang;
      Pages: 1 - 17
      Abstract: Change detection (CD) based on high-resolution remote sensing images can be used to monitor land cover changes, which is an important and challenging topic in the remote sensing field. In recent years, with the development of deep learning, CD methods based on deep learning have achieved good results in the field of CD. However, most current CD methods use single- or dual-stream networks to extract change features, which is insufficient to extract and learn bitemporal change information thoroughly. This article proposes a triple-stream network (TSNet) with cross-stage feature fusion for CD in high-resolution bitemporal remote sensing images. First, to obtain highly representative deep features in the original image, we perform feature extraction on bitemporal remote sensing images and their concatenated image with a dual-stream encoder and a single-stream encoder, respectively. Then, the bitemporal multiscale features extracted by the dual-stream encoder are input into a multistage bidirectional convolutional gated recurrent unit (MSBC_GRU) feature fusion module, allowing the network to learn the change information in a cross-stage manner. In addition, we use a dual-channel attention module to fuse the features extracted by dual- and single-stream encoders, improving the network’s ability to discriminate changed features. The effectiveness of TSNet is demonstrated with three publicly available CD datasets. The extensive experimental results demonstrate that the proposed method achieves the state-of-the-art CD performance on the above three datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • SBSS: Stacking-Based Semantic Segmentation Framework for Very
           High-Resolution Remote Sensing Image

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      Authors: Yuanzhi Cai;Lei Fan;Yuan Fang;
      Pages: 1 - 14
      Abstract: Semantic segmentation of very high-resolution (VHR) remote sensing images is a fundamental task for many applications. However, large variations in the scales of objects in those VHR images pose a challenge for performing accurate semantic segmentation. Existing semantic segmentation networks are able to analyze an input image at up to four resizing scales, but this may be insufficient given the diversity of object scales. Therefore, multiscale (MS) test-time data augmentation is often used in practice to obtain more accurate segmentation results, which makes equal use of the segmentation results obtained at the different resizing scales. However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation. Based on this behavior, a stacking-based semantic segmentation (SBSS) framework is proposed to improve the segmentation results by learning this behavior, which contains a learnable error correction module (ECM) for segmentation result fusion and an error correction scheme (ECS) for computational complexity control. Two ECS, i.e., ECS-MS and ECS-single-scale (SS), are proposed and investigated in this study. The floating-point operations (Flops) required for ECS-MS and ECS-SS are similar to the commonly used MS test and the SS test, respectively. Extensive experiments on four datasets (i.e., Cityscapes, UAVid, LoveDA, and Potsdam) show that SBSS is an effective and flexible framework. It achieved higher accuracy than MS when using ECS-MS, and similar accuracy as SS with a quarter of the memory footprint when using ECS-SS.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • CoF-Net: A Progressive Coarse-to-Fine Framework for Object Detection in
           Remote-Sensing Imagery

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      Authors: Cong Zhang;Kin-Man Lam;Qi Wang;
      Pages: 1 - 17
      Abstract: Object detection in remote-sensing images is a crucial task in the fields of Earth observation and computer vision. Despite impressive progress in modern remote-sensing object detectors, there are still three challenges to overcome: 1) complex background interference; 2) dense and cluttered arrangement of instances; and 3) large-scale variations. These challenges lead to two key deficiencies, namely, coarse features and coarse samples, which limit the performance of existing object detectors. To address these issues, in this article, a novel coarse-to-fine framework (CoF-Net) is proposed for object detection in remote-sensing imagery. CoF-Net mainly consists of two parallel branches, namely, coarse-to-fine feature adaptation (CoF-FA) and coarse-to-fine sample assignment (CoF-SA), which aim to progressively enhance feature representation and select stronger training samples, respectively. Specifically, CoF-FA smoothly refines the original coarse features into multispectral nonlocal fine features with discriminative spatial–spectral details and semantic relations. Meanwhile, CoF-SA dynamically considers samples from coarse to fine by progressively introducing geometric and classification constraints for sample assignment during training. Comprehensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Rotation-Invariant Feature Learning via Convolutional Neural Network With
           Cyclic Polar Coordinates Convolutional Layer

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      Authors: Shaohui Mei;Ruoqiao Jiang;Mingyang Ma;Chao Song;
      Pages: 1 - 13
      Abstract: Convolutional neural networks (CNNs) have been demonstrated to be powerful tools to automatically learn effective features from large datasets. Though features learned in CNNs are approximately scale-, translation-, and position-invariant, and their capacity in dealing with image rotations remains limited. In this article, a novel cyclic polar coordinate convolutional layer (CPCCL) is proposed for CNNs to handle the problem of rotation invariance for feature learning. First, the proposed CPCCL converts rotation variation into translation variation using polar coordinates transformation, which can easily be handled by CNNs. Moreover, cyclic convolution is designed to completely handle the translation variation converted from rotation variation by conducting convolution in a cyclic shift mode. Note that the proposed CPCCL is capable of generalization and can be used as a preprocessing layer for classification CNNs to learn the rotation-invariant feature. Extensive experiments over three benchmark datasets demonstrate that the proposed CPCCL can clearly handle the rotation-sensitive problem in traditional CNNs and outperforms several state-of-the-art rotation-invariant feature learning algorithms.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Hierarchical Deformable Deep Neural Network and an Aerial Image
           Benchmark Dataset for Surface Multiview Stereo Reconstruction

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      Authors: Jiayi Li;Xin Huang;Yujin Feng;Zhen Ji;Shulei Zhang;Dawei Wen;
      Pages: 1 - 12
      Abstract: Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset (https://irsip.whu.edu.cn/resources/resources_en_v2.php), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Generalized Ridge Regression-Based Channelwise Feature Map Weighted
           Reconstruction Network for Fine-Grained Few-Shot Ship Classification

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      Authors: Yangfan Li;Chunjiang Bian;Hongzhen Chen;
      Pages: 1 - 10
      Abstract: Fine-grained ship classification (FGSCR) has many applications in military and civilian fields. In recent years, deep learning has been widely used for classification tasks, and its success is inseparable from that of big data. However, ship images are valuable, with only a few images of a specific category being obtained, leading to the fine-grained few-shot ship classification problem. In addition, feature map channels contain distinct characteristics and discriminative details, which significantly influence FGSCR. Intuitively, channels with distinct characteristics should be assigned larger weights for classification, but most few-shot learning methods treat the channels equally. Therefore, we propose a generalized ridge-regression-based channelwise feature map weighted reconstruction network to address these issues. First, we reconstruct the query feature map by assigning different weights to the support feature map channels using the generalized ridge regression method. The channels with large discriminative details contribute more toward reconstruction. Second, we propose a support channel weight module to calculate the channel weight matrix used in the generalized ridge regression method. Finally, based on the reconstructed query feature map, we can calculate the reconstruction error. The reconstruction error is adopted as the distance metric. Our proposed method achieves excellent performance on the fine-grained ship, bird, aircraft, and WHU-RS19 datasets compared with other representative few-shot learning methods. Considering the limited studies on the fine-grained few-shot ship classification problem, we believe that our work is of great significance.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • MTU-Net: Multilevel TransUNet for Space-Based Infrared Tiny Ship Detection

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      Authors: Tianhao Wu;Boyang Li;Yihang Luo;Yingqian Wang;Chao Xiao;Ting Liu;Jungang Yang;Wei An;Yulan Guo;
      Pages: 1 - 15
      Abstract: Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by Earth-orbiting satellites. Due to the extremely large image coverage area (e.g., thousands of square kilometers), candidate targets in these images are much smaller, dimer, and more changeable than those targets observed by aerial- and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and $17,598$ pixel-level tiny ship annotations. Each image covers about $10,000$ km2 of area with $10 000,, times 10 000$ pixels. Considering the extreme characteristics (e.g., small, dim, and changeable) of those tiny ships in such challenging scenes, we propose a multilevel TransUNet (MTU-Net) in this article. Specifically, we design a vision Transformer (ViT) convolutional neural network (CNN) hybrid encoder to extract multilevel features. Local feature maps are first extracted by several convolution layers and then fed into the multilevel feature extraction module [multilevel ViT module (MVTM)] to capture long-distance dependency. We further propose a copy–rotate–resize–paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and-existing deep learning-based single-frame infrared small target (SIRST) methods in terms of probability of detection, false alarm rate, and intersection over union. Our code is available at https://github.com/TianhaoWu16/Multi-level-TransUNet-for-Space-based-Infrared-Tiny-ship-Detection
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Lightweight Salient Object Detection in Optical Remote-Sensing Images via
           Semantic Matching and Edge Alignment

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      Authors: Gongyang Li;Zhi Liu;Xinpeng Zhang;Weisi Lin;
      Pages: 1 - 11
      Abstract: Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote-sensing images (ORSI-SOD) are proposed. However, most methods ignore the number of parameters and computational cost brought by CNNs, and only a few pay attention to portability and mobility. To facilitate practical applications, in this article, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through $L_{2}$ loss and used for detail enhancement. Finally, starting from the highest level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods, but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76 M parameters and running with 1.7 G floating point operations (FLOPs) for $288 times 288$ inputs. Our code and results are available at https://github.com/MathLee/SeaNet.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Thick Cloud Removal Under Land Cover Changes Using Multisource Satellite
           Imagery and a Spatiotemporal Attention Network

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      Authors: Hao Liu;Bo Huang;Jiajun Cai;
      Pages: 1 - 18
      Abstract: Remote sensing satellites provide observations of the Earth’s surface, which are crucial data for applications and analyses in several fields, including agriculture, environmental protection, and sustainable development. However, the wide and frequent occurrence of clouds highly undermines the quality and availability of usable optical data, particularly low-temporal-resolution data. Although deep learning techniques have facilitated recent progress in cloud removal algorithms, thick cloud removal under changing land cover remains challenging. In this study, we propose a framework to remove thick clouds, thin clouds, and cloud shadow from Sentinel-2 images. The framework integrates the spatial detail in a Sentinel-2 reference image and the coarse spectral pattern in a near-target-date Sentinel-3 image as spatiotemporal guidance to generate missing data with land cover change information in a cloudy Sentinel-2 image. The reconstruction is performed using a spatiotemporal attention network (STAN) that adopts the self-attention mechanism, residual learning, and high-pass features to enhance feature extraction from the multisource data. The experimental results show that STAN outperforms residual u-net (ResUnet), cloud-removal network (CRN), convolutional neural network-based spatial–temporal–spectral (STS-CNN), and DSen2-CR in terms of multiple quantitative metrics and visual characteristics. The comparative experiment proves that the integration of Sentinel-3 data improves the cloud removal performance, especially in areas with distinctive and heterogeneous land cover changes under large-scale cloud cover. The experimental results also indicate high generalizability of STAN when the Sentinel-3 image is far from the target date, when transferring features to cloud removal for new images, and even with limited training data that simulates severe cloud cover.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Partial Domain Adaptation for Scene Classification From Remote Sensing
           Imagery

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      Authors: Juepeng Zheng;Yi Zhao;Wenzhao Wu;Mengxuan Chen;Weijia Li;Haohuan Fu;
      Pages: 1 - 17
      Abstract: Although domain adaptation approaches have been proposed to tackle cross-regional, multitemporal, and multisensor remote sensing applications since they do not require any human interpretation in the target domain, most current works assume identical label space across the source and the target domains. However, in real-world applications, we often transfer knowledge from a large-scale dataset with rich annotations to a small-scale target dataset with scarcity of labels. In most cases, the label space of the source domain is usually large enough to subsume that of the target domain, which is termed partial domain adaptation. In this article, we propose a new partial domain adaptation algorithm for remote sensing scene classification and our proposed method contains three major parts. First, we employ a progressive auxiliary domain module to alleviate the negative transfer effect caused by outlier classes. Second, we adopt an improved domain adversarial neural network (DANN) with multiweights to better encourage domain confusion. Last but not least, we design an attentive complement entropy regularization to improve the prediction confidence for samples and avoid untransferable samples (such as the samples belonging to outlier classes in the source domain) being mistakenly classified. We collect three common remote sensing datasets to evaluate our proposed method. Our method achieves an average accuracy of 79.36%, which considerably outperforms other state-of-the-art partial domain adaptation methods with an average accuracy improvement of 1.90%–12.45% and attaining a 13.67% gain compared to the straightforward deep learning model (ResNet-50). The experiment results indicate that our approach shows promising prospects for solving more general and practical domain adaptation problems where the label space of the source domain subsumes that of the target domain.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Progressive Learning for Unsupervised Change Detection on Aerial Images

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      Authors: Yuan Zhou;Xiangrui Li;Keran Chen;Sun-Yuan Kung;
      Pages: 1 - 13
      Abstract: This article focuses on unsupervised methods for optical aerial image change detection. Existing unsupervised change detection techniques are mainly categorized as patch-based methods and transfer-learning-based methods. However, the first type ignores the spatial information in the images, and the second type may introduce new errors due to knowledge extracted from additional datasets. To effectively tackle these problems, we propose an unsupervised progressive learning framework (UPLF). We first use original estimated change maps as the labeled samples and choose the reliable regions from samples to train the network. We then propose a progressive learning method to expand the reliable labeled region. Briefly, we apply a label selection filter to filter out incorrect change information from the regions to help rectify incorrect labeling in the regions. This leads to a more reliable labeled region and thus, in turn, more accurate detection results. Compared with the patch-based and transfer-learning-based unsupervised techniques, our method takes the entire map as the training sample to avoid the problem associated with using small patches; moreover, our iterative and progressive methods further enhance the change detection performance without involving external knowledge. Indeed, based on our experimental results on the real datasets, the proposed method demonstrates highly competitive performance compared with the state-of-the-art.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Automatic Urban Scene-Level Binary Change Detection Based on a Novel
           Sample Selection Approach and Advanced Triplet Neural Network

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      Authors: Hong Fang;Shanchuan Guo;Xin Wang;Sicong Liu;Cong Lin;Peijun Du;
      Pages: 1 - 18
      Abstract: Change detection is a process of identifying changed ground objects by comparing image pairs obtained at different times. Compared with the pixel-level and object-level change detection, scene-level change detection can provide the semantic changes at image level, so it is important for many applications related to change descriptions and explanations such as urban functional area change monitoring. Automatic scene-level change detection approaches do not require ground truth used for training, making them more appealing in practical applications than nonautomatic methods. However, the existing automatic scene-level change detection methods only utilize low-level and mid-level features to extract changes between bitemporal images, failing to fully exploit the deep information. This article proposed a novel automatic binary scene-level change detection approach based on deep learning to address these issues. First, the pretrained VGG-16 and change vector analysis are adopted for scene-level direct predetection to produce a scene-level pseudo-change map. Second, pixel-level classification is implemented by using decision tree, and a pixel-level to scene-level conversion strategy is designed to generate the other scene-level pseudo-change map. Third, the scene-level training samples are obtained by fusing the two pseudo-change maps. Finally, the binary scene-level change map is produced by training a novel scene change detection triplet network (SCDTN). The proposed SCDTN integrates a late-fusion subnetwork and an early fusion subnetwork, comprehensively mining the deep information in each raw image as well as the temporal correlation between two raw images. Experiments were performed on a public dataset and a new challenging dataset, and the results demonstrated the effectiveness and superiority of the proposed approach
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Local Adaptive Prior-Based Image Restoration Method for Space Diffraction
           Imaging Systems

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      Authors: Shikai Jiang;Jianming Hu;Xiyang Zhi;Wei Zhang;Dawei Wang;Xiaogang Sun;
      Pages: 1 - 10
      Abstract: Thin-film diffractive optical elements (DOEs) have considerable potential to be used in the field of high-resolution remote sensing imaging satellites because of advantages such as a large aperture, small volume, lightness, wide tolerance range of surface shape, and easy replication. However, there are problems associated with thin-film diffraction imaging, including space variation, serious blur, and low contrast, which result in insufficient imaging quality with regard to traditional optical system requirements. To address this, a local adaptive prior-based image restoration method is proposed for thin-film diffraction imaging systems. An entire degraded image was divided into several isohalo regions based on imaging characteristics. Then, the regularization constraints were adaptively selected and updated according to the local scene prior characteristics. Additionally, the system parameters in the corresponding field of view were used as input to restore each subregion. In particular, the diffraction efficiency (DIE) was introduced into the model to remove the nondesign level background radiation. The experimental results show that the proposed algorithm can effectively improve the image quality of a thin-film diffraction imaging system, including space variation correction, clarity enhancement, and background radiation suppression. Furthermore, a DIE of less than 60% was found to significantly impact the final image products.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Lightweight Stepless Super-Resolution of Remote Sensing Images via
           Saliency-Aware Dynamic Routing Strategy

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      Authors: Hanlin Wu;Ning Ni;Libao Zhang;
      Pages: 1 - 17
      Abstract: Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of subimage patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good tradeoff between performance and complexity. The code is available at https://github.com/hanlinwu/SalDRN.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Change Detection Based on Supervised Contrastive Learning for
           High-Resolution Remote Sensing Imagery

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      Authors: Jue Wang;Yanfei Zhong;Liangpei Zhang;
      Pages: 1 - 16
      Abstract: Change detection (CD) is a challenging task on high-resolution bitemporal remote sensing images. Many recent studies of CD have focused on designing fully convolutional Siamese network architectures. However, most of these methods initialize their encoders by random values or an ImageNet pretrained model, without any prior for the CD task, thus limiting the performance of the CD model. In this article, the novel supervised contrastive pretraining and fine-tuning CD (SCPFCD) framework, which is made up of two cascaded stages, is presented to train a CD network based on a pretrained encoder. In the first supervised contrastive pretraining stage, the encoder of the Siamese network is asked to solve a joint pretext task introduced by the proposed CDContrast pretraining method on labeled CD data. The proposed CDContrast pretraining method includes land contrastive learning (LCL), which is based on supervised contrastive learning, and proxy CD learning. The LCL focuses on learning the spatial relationships among the land cover from bitemporal images by solving a land contrast task, while the proxy CD learning performs a proxy CD task on the top of the upsampling projector to avoid local optima for the LCL and learn features for the CD. Then, in the second fine-tuning stage, the whole Siamese network initialized with the pretrained encoder is fine-tuned to perform the CD task in an end-to-end manner. The proposed SCPFCD framework was verified with three CD datasets of high-resolution remote sensing images. The extensive experimental results consistently show that the proposed framework can effectively improve the ability to extract change information for Siamese networks.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Performance Simulation Theory of Low-Level Wind Shear Detections Using an
           Airborne Coherent Doppler Lidar Based on RTCA DO-220

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      Authors: Shumpei Kameyama;Masashi Furuta;Eiichi Yoshikawa;
      Pages: 1 - 12
      Abstract: The performance simulation theory of low-level wind shear (LLWS) detection using an airborne coherent Doppler lidar (CDL) is shown. The simulation theory that this performance is based on is analogous to the turbulence and wind shear detections with an airborne Doppler radar, as specified in the radio technical commission for aeronautics (RTCA) document (DO)-220. Prior theoretical studies of CDL regarding: 1) signal-to-noise ratio (SNR) equation; 2) the relation between SNR and wind sensing performance; and 3) atmospheric parameters are fully utilized. An example simulation result under a practical condition satisfies the requirements to specify minimum operational performance standards (MOPS) in DO-220. Further, the simulation result is well aligned with past experimental results on the detectable range of LLWS. The results of this study can be utilized to establish MOPS for LLWS detections using an airborne CDL in the future.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Geometric Calibration Method Without a Field Site of the GF-7 Satellite
           Laser Relying on a Surface Mathematical Model

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      Authors: Junfeng Xie;Ren Liu;Xinming Tang;Xiaomeng Yang;Junze Zeng;Fan Mo;Yongkang Mei;
      Pages: 1 - 14
      Abstract: The GaoFen-7 (GF-7) satellite, which is used for Earth observations, is equipped with China’s first civil full-waveform laser altimeter. The geometric calibration of the GF-7 satellite laser is vital for ensuring its long-term stability and highly precise global measurements. In this study, a geometric of calibrating satellite laser is proposed. Notably, this method does not require an outdoor calibration field but instead relies on a mathematical surface model. This calibration method converts that to calibrate laser pointing angle error in traditional calibration method to calibrate the three-axis rotation angle error between the laser frame and the satellite body fixed-frame. Assuming that the laser footprints always fall on a fit spatial plane, a new satellite laser geometry calibration model is established. In addition, the GF-7 satellite laser is calibrated by using airborne light detection and ranging (LiDAR) point clouds collected from the sloped terrain of Xinjiang. Taking the laser footprints that are captured by the laser ground detectors as the true values, the positioning error of GF-7 beam 1 is improved from 430 m before calibration to 1.8 m after calibration. And the beam 2 positioning error is improved from 1025 to 6.4 m. Finally, according to the surface elevation of the laser footprints that were measured by a real-time kinematic, the elevation error of the GF-7 laser on flat terrain was verified to be 0.14 m after calibration. In summary, the calibration method proposed in this study is effective and feasible and provides a new method for satellite laser calibration.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Detection of Signal and Ground Photons From ICESat-2 ATL03 Data

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      Authors: Xiangxi Tian;Jie Shan;
      Pages: 1 - 14
      Abstract: The Advanced Topographic Laser Altimeter System (ATLAS) laser altimeter aboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2) can measure the elevation of the Earth’s surface with unprecedented spatial detail. However, the quality of the derived signal and ground photons depends on the signal-to-noise ratio and canopy coverage. Current algorithms underperform for data collected during daytime over mountain areas with dense canopy. We demonstrate a novel procedure for signal photon detection and subsequent ground photon detection from ICESat-2 ATL03 data. We first introduce a gravity-based density model to characterize the anisotropic properties of photon distribution. Through jointly using the photon densities from the weak–strong beam pair, we are able to find key photons that have high probability being signals. A directional regional growing approach then takes these key photons as seeds to label all remaining signal photons. Finally, we introduce a weighted iterative median filter (WIMF) algorithm to identify ground photons whose height is closest to the estimated ground surface. A total of 36 ATL03 beams of two entire counties in USA are used for test and evaluation. Compared to the ATL03 and ATL08 algorithms, our signal photon finding method is more robust to the variation of topography, canopy coverage, and data collection time. Remarkably, the mislabeling caused by the after-pulsing effect does not present in our detected signal photons. Comparing current ATL03 and ATL08 products, the detected ground photons from our method are more consistent with reference to the 3DEP DEM, especially for strong beam data collected during daytime in dense canopy, high relief areas.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Exploring Photon-Counting Laser Altimeter ICESat-2 in Retrieving LAI and
           Correcting Clumping Effect

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      Authors: Da Guo;Ronghai Hu;Xiaoning Song;Xinhui Li;Hengli Lin;Yanan Zhang;Liang Gao;Xinming Zhu;
      Pages: 1 - 9
      Abstract: The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a unique multibeam photon counting approach to acquire a near-continuously sampled profile and provides more precise technology for mapping the leaf area index (LAI) at the global scale. The inversion accuracy of LAI is affected by the clumping effect, which has been an open question for spaceborne laser scanning (SLS). Here, we present a segmented method based on the path length distribution model to calculate the clumping-corrected LAI independently using ICESat-2 data. The results showed that the LAI derived by the proposed method with a 200 m segment was consistent with the airborne laser scanning (ALS)-derived LAI, with a root mean squared error (RMSE) of 0.37. A satisfactory agreement (RMSE $=1.03$ ) was also shown between moderate resolution imaging spectroradiometer (MODIS) LAI and ICESat-2 LAI. Moreover, the LAI derived by the proposed method was on average 31.72% higher than the LAIe derived by Beer’s law, which indicated that the proposed method achieved the purpose of correcting the clumping effect. The gap probability was calculated by the 200 m moving window and the path length distribution was obtained by the 1 m moving window as the model input had the highest accuracy. In addition, the limitation of the point cloud data and the time lag of ICESat-2 acquisitions and ALS observations may affect the inversion accuracy of LAI. This study proposed a feasible way to correct the clumping effect and invert LAI independently using ICESat-2 data, which has the potential to characterize vegetation structure precisely at regional and global scales.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • A Content-Adaptive Hierarchical Deep Learning Model for Detecting
           Arbitrary-Oriented Road Surface Elements Using MLS Point Clouds

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      Authors: Siyun Chen;Zhenxin Zhang;Hao Ma;Liqiang Zhang;Ruofei Zhong;
      Pages: 1 - 16
      Abstract: Accurate and automatic detection of road surface element (such as road marking or manhole cover) information is the basis and key to many applications. To efficiently obtain the information of road surface element, we propose a content-adaptive hierarchical deep learning model to detect arbitrary-oriented road surface elements from mobile laser scanning (MLS) point clouds. In the model, we design a densely connected feature integration module (DCFM) to connect and reorganize feature maps of each stage in the backbone network. Besides, we propose a hierarchical prediction module (HPM) to innovatively use the reorganized feature maps to recognize different types of road surface elements, and thus, semantic information of road surface element can be adaptively expressed on multilevel feature maps. We also add a cascade structure (CS) in the head of model to detect the target efficiently, which can learn the offset between the predicted minimum bounding box of road surface element and ground truth. In experiments, we prove that the proposed method mainly contributed by HPM can maintain robust detection performance, even in the cases of unbalanced category number or overlapping of road surface elements. The experiments also prove that the proposed DCFM can improve the recognition effects of small targets. The CS for predicting boundary offset can detect each target more accurately. We also integrate the designed modules into some rotation detectors, e.g., the EAST and R3Det, and achieve the state-of-the-art results in three road scenes with different categories and uneven distribution of road surface elements, which further shows the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Prototype-Guided Multitask Adversarial Network for Cross-Domain LiDAR
           Point Clouds Semantic Segmentation

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      Authors: Zhimin Yuan;Ming Cheng;Wankang Zeng;Yanfei Su;Weiquan Liu;Shangshu Yu;Cheng Wang;
      Pages: 1 - 13
      Abstract: Unsupervised domain adaptation (UDA) segmentation aims to leverage labeled source data to make accurate predictions on unlabeled target data. The key is to make the segmentation network learn domain-invariant representations. In this work, we propose a prototype-guided multitask adversarial network (PMAN) to achieve this. First, we propose an intensity-aware segmentation network (IAS-Net) that leverages the private intensity information of target data to substantially facilitate feature learning of the target domain. Second, the category-level cross-domain feature alignment strategy is introduced to flee the side effects of global feature alignment. It employs the prototype (class centroid) and includes two essential operations: 1) build an auxiliary nonparametric classifier to evaluate the semantic alignment degree of each point based on the prediction consistency between the main and auxiliary classifiers and 2) introduce two class-conditional point-to-prototype learning objectives for better alignment. One is to explicitly perform category-level feature alignment in a progressive manner, and the other aims to shape the source feature representation to be discriminative. Extensive experiments reveal that our PMAN outperforms state-of-the-art results on two benchmark datasets.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Target Detection and Location by Fusing Delay-Doppler Maps

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      Authors: Yan Li;Songhua Yan;Jianya Gong;
      Pages: 1 - 14
      Abstract: The use of delay-Doppler maps (DDMs) measured by Global Navigation Satellite System Reflectometry (GNSS-R) for target detection and location is a hot issue because of the potentially global coverage and short revisit periods of GNSS-R satellite missions. Existing researches have explored the detection of oil rigs, oil spills, sea ice, and other targets with spaceborne GNSS-R. However, these researches may not fully consider the advantages of multiple GNSS-R satellites, for example, Cyclone Global Navigation Satellite System (CYGNSS) which has eight satellites. Two problems of using multiple GNSS-R satellites for detection and location are how to estimate the sea clutter that is used to cancel the clutter in DDMs and how to remove location ambiguity caused by the transformation from the delay-Doppler (DD) domain to the spatial domain. In this article, an oil rig is taken as an example for detection and location with multiple GNSS-R satellites. To distinguish the oil rig from sea clutter, this article uses subspace projection to estimate the clutter and subtract the estimation from the DDMs. Moreover, after the clutter cancellation, this article projects the potential oil rig coordinates in the DD domain into a uniform coordinate system and removes the location ambiguity based on multisatellites fusion. The measured DDMs collected by CYGNSS are employed to conduct experiments to validate the feasibility of the proposed methods for detection and location with DDMs. By subtracting the estimated sea clutter from DDMs, the potential oil rig can be indicated in the DD domain. Two location experiments can calculate the unambiguous coordinate of the oil rig with a deviation less than 4 km.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Direct Exact Nonlinear Broadband Seismic Amplitude Variations With Offset
           Inversion for Young’s Modulus

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      Authors: Xiao Chen;Zhaoyun Zong;Yinghao Zuo;
      Pages: 1 - 16
      Abstract: Young’s modulus and the Poisson ratio are critical for shale reservoir identification, and oil and natural gas detection since they are elastic parameters that can reflect the fracturing properties of subsurface rocks. The broadband inversion approach can make full use of the seismic ultralow-frequency information by combining it with the inversion results in the complex frequency domain. This plays an important role in the stability and reliability of the inversion, improving its resolution. Nevertheless, at present, the amplitude variation with offset (AVO) inversions of these parameters is mostly in the form of linear approximations, and there are few inversion methods of exact nonlinear equations or broadband. Considering that the nonlinear equation of reflection coefficient compared with the linear approximation has higher precision and fewer assumption conditions, and the low-frequency information of the complex frequency-domain inversion can improve the reliability of the inversion results, we derive an exact reflection coefficient equation for Young’s modulus, the Poisson ratio coefficient, and the density based on the exact Zoeppritz equation and apply it to the broadband complex domain nonlinear prestack inversion. Young’s modulus and the Poisson ratio coefficients estimated by the new inversion method provide a theoretical basis for evaluating the reservoir brittleness and compressibility, as well as a new approach for shale gas reservoir prediction and sweet spot identification. We tested the accuracy and rationality of this method with both synthetic and field data examples.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Long-Term Spatial–Temporal Evolution of Seismicity of the 2010 Ms 7.1
           Yushu, Qinghai, China Earthquake

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      Authors: Yafen Huang;Hongyi Li;Yuhu Ma;Jianxin Ma;
      Pages: 1 - 9
      Abstract: On 14 April 2010, a destructive earthquake (Ms 7.1) occurred along the Ganzi–Yushu fault (GYF), which is the boundary between the Songpan–Ganzi and Qiangtang Blocks of the eastern Tibetan Plateau. In this study, we present an effective workflow for microearthquake detection and location that assembles three main methods including a graphics processing unit-based match and locate technology (GPU M&L), a deep-neural-network-based noise-reduction technique (DeepDenoiser), and a U-net-based phase picking method (PhaseNet). Following this workflow, a high-resolution catalog has been constructed based on continuous seismic data from 2008 to 2018 recorded by 13 seismic stations deployed by Qinghai seismic network in Yushu, Qinghai. The GPU M&L is first applied to detect earthquakes which are usually hard to pick by routine methods due to their low signal-to-noise ratios. Then the DeepDenoiser is applied to remove noises from events we detected by GPU M&L, providing a better picking of seismic phases. The PhaseNet and hypoDD are conducted to pick seismic phases and relocate detected events, respectively. The spatial–temporal evolution of the foreshock sequence of the Yushu earthquake suggests that a triggered cascade model is responsible for the nucleation of the mainshock. The mainshock and aftershocks southeast of the mainshock mainly rupture on the primary northwest (NW)-trending GYF while the foreshocks and aftershocks on the NW segment rupture along the northeast (NE)-trending fault normal to the strike of the GYF. The average cumulative slip rate of the GYF estimated from six repeating earthquakes is $7.4~pm ~1.7$ mm/year, which is consistent with the geodetic and geological observations.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • CTMFNet: CNN and Transformer Multiscale Fusion Network of Remote Sensing
           Urban Scene Imagery

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      Authors: Pengfei Song;Jinjiang Li;Zhiyong An;Hui Fan;Linwei Fan;
      Pages: 1 - 14
      Abstract: Semantic segmentation of remotely sensed urban scene images is widely demanded in areas such as land cover mapping, urban change detection, and environmental protection. With the development of deep learning, methods based on convolutional neural networks (CNNs) have been dominant due to their powerful ability to represent hierarchical feature information. However, the limitations of the convolution operation itself limit the network’s ability to extract global contextual information. With the successful use of transformer in computer vision in recent years, transformer has shown great potential for modeling global contextual information. However, transformer is not sufficiently capable of capturing local detailed information. In this article, to explore the potential of the joint CNN and transformer mechanism for semantic segmentation of remotely sensed urban scenes, we propose a CNN and transformer multiscale fusion network (CTMFNet) based on encoding–decoding for urban scene understanding. To couple local–global context information more efficiently, we designed a dual backbone attention fusion module (DAFM) to couple the local and global context information of the dual-branch encoder. In addition, to bridge the semantic gap between scales, we built a multi-layer dense connectivity network (MDCN) as our decoder. The MDCN enables the full flow of semantic information between multiple scales to be fused with each other through upsampling and residual connectivity. We conducted extensive subjective and objective comparison experiments and ablation experiments on both the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen and ISPRS Potsdam datasets. Numerous experimental results have proven the superiority of our method compared to currently popular methods.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Seismic Image Dip Estimation by Multiscale Principal Component Analysis

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      Authors: Shaojiang Wu;Yibo Wang;
      Pages: 1 - 10
      Abstract: Dip estimation of geological structures plays an important role in geophysical applications. Principal component analysis (PCA) is a common approach to estimating local dips by decomposing the local gradients of a seismic migration image and obtaining its principal eigenvector. However, PCA is difficult to obtain robust and high-resolution dip estimations for low signal-to-noise ratio (SNR) migration images, while multiscale schemes in digital image processing can achieve a better compromise between noise robustness and dip resolution. Therefore, we propose to adopt a multiscale PCA (MPCA) method coupled with a propagation-weight-based fusion mechanism for seismic dip estimation of low SNR migration image. MPCA consists of three steps: 1) constructing an image pyramid by repeating the low-pass filter from fine to coarse scales; 2) estimating the dip using the PCA method at each scale of the image pyramid; and 3) fusing the multiscale dip estimations using propagation weights from coarse to fine scales. We test the MPCA method on an omnidirectional dip pattern and three seismic migration images and compare with the conventional PCA and multiscale methods. The results demonstrate that MPCA yields robust and high-resolution dip estimations for low SNR seismic migration images.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Seismic Random Noise Simultaneous Attenuation in the Time–Frequency
           Domain Using Lp-Variation and γ Norm Constraint

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      Authors: Liangsheng He;Hao Wu;Xiaotao Wen;
      Pages: 1 - 17
      Abstract: Sparse low-rank denoising methods are widely applied for seismic random noise attenuation. Due to the poor structural sparsity and poor low rank of the traditional method, random noises, effective signal loss, and poor continuity still exist. To overcome these barriers, a multitrace seismic random noise simultaneous attenuation method in the time–frequency using the Lp-variation and ${gamma }$ norm constraint is proposed. This approach uses Lp-variation regularization to describe the structural sparsity of seismic data in the time–frequency domain. The structural sparsity can obtain the structural similarity of adjacent traces in the time–frequency domain. This similarity can improve the continuity of events and can further suppress low-amplitude random noise. Besides, the approach utilizes the ${gamma }$ norm to constrain the low rank of seismic data in the time–frequency domain. The ${gamma }$ norm can obtain more low-rank information than the nuclear norm. More low-rank information can improve the overall suppression effect of random noise. The Lp-variation and ${gamma }$ norm constraints are used to construct the objective function. The alternating direction method of multipliers algorithm, the difference of convex programming, and singular value decomposition are utilized to obtain the attenuation algorithm. Both synthetic and field data tests prove that the proposed method has better denoising and effective signal protection ability.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Time-Delay Estimation of Microseismic DAS Data Using Band-Limited
           Phase-Only Correlation

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      Authors: Shaojiang Wu;Yibo Wang;Xing Liang;
      Pages: 1 - 9
      Abstract: Time-delay estimation is a critical step in many geophysical applications. Conventional approaches are mainly based on the cross correlation of waveforms in time domain but show strong distortions with multiple oscillations and side lobes. We propose a band-limited phase-only correlation (POC) algorithm for time-delay estimation. The algorithm involves the following key steps: 1) transforming 1-D waveform into 2-D time–frequency spectra using S-transform; 2) calculating the band-limited POC function of the transformed spectra; and 3) measuring time delays by analyzing POC coefficient. We demonstrate the effectiveness of the proposed algorithm using synthetic and real microseismic fiber-optic distributed acoustic sensing (DAS) datasets. Results show that POC can effectively and accurately estimate the time delays of waveforms. Compared with the performance of the conventional cross correlation method in time domain, the proposed method has three main advantages: 1) better identification of event and noise waveforms; 2) lower uncertainty of narrow correlation peaks; and 3) weaker distortions with small oscillations and side lobes.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Generating Azimuth–Reflection Angle Gathers From Reverse Time Migration
           Using the High-Dimensional Local Phase Space Approximation of Seismic
           Wavefields

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      Authors: Feipeng Li;Jinghuai Gao;Zhaoqi Gao;Chuang Li;Qingzhen Wang;Wenbo Sun;Zongben Xu;
      Pages: 1 - 14
      Abstract: Amplitude-preserving angle gathers are ideal inputs for seismic prestack inversion. However, due to the limitation of computational efficiency, generating subsurface azimuth–reflection angle gathers from 3-D seismic imaging is still a very difficult task. In this article, we propose a new method to generate azimuth–reflection angle gathers from 3-D reverse time migration (RTM). The proposed method approximately reconstructs the source wavefield using high-dimensional wavelets and the excitation information. After using directional vectors to calculate the subsurface observation angles and applying the cross correlation imaging condition, we can generate azimuth–reflection angle gathers by angle binning. Without storing source wavefields or reconstructing source wavefields using boundary conditions, the proposed method has high computational efficiency. Numerical experiments on a synthetic model and a real marine seismic dataset demonstrate that compared with the excitation amplitude imaging condition, the proposed method can generate azimuth–reflection angle gathers with continuous complete events and high signal-to-noise ratio. The image quality and resolution of angle gathers are significantly improved. At the same time, the computational complexity does not increase much.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Frequency-Dependent Nonlinear AVO Inversion for Q-Factors in Viscoelastic
           Media

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      Authors: Xiao Chen;Zhaoyun Zong;Yaming Yang;Yongjian Zeng;
      Pages: 1 - 17
      Abstract: Viscoelastic theory-based frequency-dependent amplitude variation with offset (AVO) inversions are a useful tool for identifying fluids based on their dispersion properties. When used to identify reservoir oil and gas qualities, the quality factors ( $Q$ -factors) derived from the inversion have produced good results. But currently, the majority of $Q$ -factor inversions are linear inversions based on linear approximations, whereas nonlinear inversions based on exact equations with higher precision and fewer assumptions are only occasionally carried out. Meanwhile, in broadband seismic inversion, the low-frequency model estimated by complex frequency inversion can fully utilize seismic data’s low-frequency information and provide improved robustness and rationality for inversion. Therefore, we derive a frequency-dependent reflection coefficient equation that varies with angle by replacing the nearly constant $Q$ model suggested by Aki and Richards into the exact Zoeppritz equation and simplifying. Additionally, using the Bayesian framework as a foundation, we created a novel two-stage broadband frequency-dependent nonlinear inversion approach for $Q$ -factors that can estimate $Q$ -factors, produce accurate fluid identification results, and serve as a solid foundation for oil and gas exploration and reservoir identification. Utilizing examples from both synthetic and real-world data, we verify the applicability of oil and gas indicators.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Deep Learning Vertical Resolution Enhancement Considering Features of
           Seismic Data

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      Authors: Yang Gao;Dongfeng Zhao;Tinghui Li;Guofa Li;Shuwen Guo;
      Pages: 1 - 13
      Abstract: The resolution of seismic data determines the ability to characterize individual geological structures in a seismic image. Sparse spike inversion (SSI) is an effective approach for improving the resolution of seismic data. However, the basic assumption of SSI is that the strong reflectivity of the formation is sparse, which may not be a reasonable fit for weak thin-layer reflections. In this study, we propose a deep learning-based method to reconstruct high-resolution seismic data by combining information from the longitudinal reflectivity distribution and lateral geological structure features in the field data. A U-shaped network that fuses residual block and attention mechanism is used to learn the relationship between low resolution and high resolution. In addition, we use a hybrid loss function that combines ${ell _{1}}$ loss and structural similarity (SSIM) loss to optimize the network parameters for better distinguishing the geometrical features characterized by structural amplitude changes. To train the network, we adopt a workflow to automatically generate numerous 2-D low-resolution data and their corresponding high-resolution data. In this workflow, the prior information, such as statistical reflectivity distribution of well logs and structural features of the data are considered. The synthetic data and field data tests show that our method can work well compared to the traditional method even though only a few well logs are available. Especially in the field data example, our method recovers thin layers better and yields laterally more consistent high-resolution results.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • 3-D Forward Modeling of Transient EM Field in Rough Media Using Implicit
           Time-Domain Finite-Element Method

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      Authors: Yunhe Liu;Luyuan Wang;Changchun Yin;Xiuyan Ren;Bo Zhang;Yang Su;Zhihao Rong;Xinpeng Ma;
      Pages: 1 - 11
      Abstract: In a heterogeneous medium (usually called a rough medium) with fractured formations, the propagation of an electromagnetic (EM) field is a type of subdiffusion. Current mainstream geophysical EM data processing methods cannot be applied to data acquired on heterogeneous Earth, as they are not governed by the classic diffusion theory. To evaluate the influence of roughness on the transient EM (TEM) signal for a complex model and contribute to data inversion, we proposed a novel three-dimensional (3-D) forward modeling scheme for TEM in rough media. First, we derived the governing equation with a fractional-order time derivative for the subdiffusion of EM waves in rough media. Then, we proposed a novel time discretization using an unequal step length for the Caputo operator, which significantly reduces the total number of time steps. Finally, an implicit time-domain finite-element method using unstructured tetrahedron discretization was adopted to solve the 3-D forward problem. Furthermore, an efficient time segmentation strategy combined with parallel RHS construction was proposed to accelerate modeling. The numerical results prove that the proposed method is accurate and efficient, and will be a powerful numerical method for analyzing TEM wave propagation and processing TEM data in areas with multiscale fractures or porosity.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Least-Squares Full-Wavefield Reverse Time Migration Using a Modeling
           Engine With Vector Reflectivity

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      Authors: Han Wu;Shaoping Lu;Xintong Dong;Xiaofan Deng;Rui Gao;
      Pages: 1 - 12
      Abstract: Conventional least-squares reverse time migration (LSRTM) generally involves a de-migration operator based on the first-order scattering approximation (Born modeling), which can only simulate the seismograms containing the primary reflected wave. When the input observed seismograms contain “redundant information” (especially multiples), crosstalk may occur in the imaging results. Therefore, we develop a least-squares full-wavefield reverse time migration (LSFWM), which is implemented based on a two-way modeling engine with vector reflectivity and the corresponding adjoint sensitive kernel. This modeling engine is modified from the variable density acoustic wave equation and can simulate the subsurface wavefield containing the primaries and multiples only by giving the accurate or estimated subsurface reflectivity and velocity. Theoretically, this LSFWM approach can eliminate the influence of “redundant information” on imaging and provide higher-quality imaging results compared to conventional LSRTM. In addition, since the modeling engine is based on vector reflectivity, the imaging results produced by the LSFWM are also vectorized, which can give more information about the subsurface structures, especially steep structures. And the imaging results produced by the LSFWM can accurately depict the subsurface reflectivity. These are helpful to obtain the information on subsurface structure and physical properties more clearly.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Improved Seismic Residual Diffracted Multiple Suppression Method Based on
           Object Detection and Image Segmentation

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      Authors: Xingyu Tian;Wenkai Lu;Yanda Li;Jinpeng Liu;Mingrui Zhong;Hongxun Pan;Bowu Jiang;
      Pages: 1 - 13
      Abstract: Seismic multiple is one of the most common noises in marine seismic data, which heavily affects subsequent processing and interpretation. To eliminate the influence of seismic multiples, many methods have been developed, while surface-related multiple elimination (SRME) is one of the most widely deployed methods. However, results of SRME always contain a few strong residual diffracted multiples (RDMs) in practice because of the unprecise prediction of diffracted multiples compared to reflection multiples. If we try to apply further multiple suppression methods to SRME results, it not only tends to damage the signals, but also spends lots of unnecessary computations where there is no RDM. In this article, we propose an improved RDM suppression method based on object detection and image segmentation. First, we employ an object detection network to locate bounding boxes containing RDMs in the SRME results. Then a threshold-based image segmentation method is utilized to identify regions of strong RDMs in the detected boxes. According to the segmentation results, parameters for weak multiples and strong multiples are provided for the adaptive multiple subtraction (AMS) in different regions to generate different results. At last, we combine the suppression results of strong RDMs and weak RDMs as the final results. Application on field data demonstrates that our method is able to suppress RDMs with little loss of signal.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • RFloc3D: A Machine-Learning Method for 3-D Microseismic Source Location
           Using P- and S-Wave Arrivals

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      Authors: Yangkang Chen;Alexandros Savvaidis;Sergey Fomel;Omar M. Saad;Yunfeng Chen;
      Pages: 1 - 10
      Abstract: Passive seismic source location imaging is important to various scientific and engineering research topics spanning from unconventional reservoir development in exploration seismology to seismic hazard prevention in the earthquake seismology community. The emerging machine-learning (ML) techniques enable the location of passive seismic sources with unprecedented efficiency and accuracy. Most of the state-of-the-art ML methods are based on waveforms, as required by the most popular convolutional neural network (CNN) architecture, which is prone to the sensitivity of velocity models. Here, we present a traveltime-based ML method, RFloc3D, to locate passive seismic sources from manually or automatically picked P- and S-wave arrivals. The proposed method is similar to traditional traveltime-based location methods, where the inverse mapping from arrival times to the passive source location is obtained by inverting a nonlinear inverse problem, but differs in leveraging the random forest (RF) method to learn the inverse mapping relation from numerous eikonal-based forward simulations. Details and analyses of the proposed RFloc3D method are illustrated based on a microseismic monitoring setup. Numerical and real data examples show that the proposed method is capable of real-time location. The inclusion of S-wave arrivals, most importantly, the differential time between P- and S-wave arrivals, helps significantly to reduce the depth error (e.g., decreasing the mean absolute error (MAE) to a half) of the located sources.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Wavefield Separation Algorithm of Helmholtz Theory-Based Discontinuous
           Galerkin Method Using Unstructured Meshes on GPU

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      Authors: Jiandong Huang;Dinghui Yang;Xijun He;
      Pages: 1 - 10
      Abstract: In the field of geophysics, the Helmholtz decomposition (HD) formula is mainly numerically discretized by the finite difference method (FDM), which limits its application to a uniform regular grid only. Few scholars note wavefield separation on unstructured grids. In this study, we aim to develop a wavefield separation algorithm to separate P- and S-wavefields on nonuniform grids. Our scheme is based on an isotropic elastic wave equation. We first transform the HD formula into a weak integral form using the discontinuous Galerkin method (DGM). Then, we consider two types of unstructured meshes—triangle and quadrangle, which are more suitable for complex structures. Moreover, to reduce time costs, the single graphic processor unit (GPU) device is used to improve the computational efficiency. We perform a unified DGM operator by transforming unstructured triangles and quadrangles into standard reference elements using coordinate transformation. Our proposed HD operator enables us to effectively separate P- and S-wavefields on unstructured meshes. We carry out the wavefield separation simulation and calculate the numerical solutions in the homogeneous carbonate model, Graben model, and SEG/EAGE model. The homogeneous model verifies the correctness, availability, and superiority of our proposed separated operator, and the other numerical results show excellent performance for P/S-wavefield separation on unstructured meshes.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Weighted Diffraction-Based Migration Velocity Analysis of Common-Offset
           GPR Reflection Data

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      Authors: Yu Liu;James Irving;Klaus Holliger;
      Pages: 1 - 9
      Abstract: Migration focusing analysis of diffractions is an increasingly important tool for estimating the large-scale subsurface velocity structure from surface-based common-offset ground-penetrating radar (GPR) reflection data. We present a weighting strategy, whose aim is to improve the reliability of estimations of the root-mean-square (rms) velocity obtained using a local semblance focusing measure. In this regard, we increase the resolution of the inferred semblance spectra through a weighting function that varies in accordance with the sensitivity of a diffraction curve to changes in velocity. The weighting function is derived from coherency and slope attributes of the diffracted wavefield components. To demonstrate the viability of our proposed method, we consider its application in two synthetic test cases and one field GPR dataset. Compared with conventional unweighted local semblance spectra, their weighted counterparts allow for a significantly increased resolution and correspondingly reduced picking uncertainty.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Regeneration-Constrained Self-Supervised Seismic Data Interpolation

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      Authors: Aoqi Song;Changpeng Wang;Chunxia Zhang;Jiangshe Zhang;Deng Xiong;Xiaoli Wei;
      Pages: 1 - 10
      Abstract: Seismic data interpolation is an indispensable part of seismic data processing. In recent years, deep-learning-based interpolation algorithms for seismic data have become popular due to their high accuracy. However, a considerable amount of work has focused on the migration of concepts and algorithms in deep-learning-based methods while ignoring the implicit properties of seismic data itself. In this article, we propose the regeneration prior, which is an implicit property of seismic data with respect to the interpolation function, and are used for self-supervised seismic data interpolation tasks. In mathematical form, the regeneration prior can be considered as a regular term describing the structure of the seismic data. Theoretically, the regeneration prior is a necessary condition to obtain an optimal interpolation function. Experimentally, the proposed method achieves significant improvement in accuracy and intuitive visualization in comparison with advanced unsupervised or self-supervised methods. In addition, we provide an intuitive interpretation of the regeneration prior, and our study shows that the regeneration prior plays an anti-overfitting structuring role in the parameter learning process of the interpolation function. Finally, we analyze the robustness of the regeneration prior. The experimental results show that the performance of the regeneration prior is stable despite the fact that the hyperparameters associated with the regeneration prior are perturbed in a considerable range.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
  • Explainable Deep Learning for Supervised Seismic Facies Classification
           Using Intrinsic Method

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      Authors: Kyubo Noh;Dowan Kim;Joongmoo Byun;
      Pages: 1 - 11
      Abstract: Deep-learning (DL) techniques have been proposed to solve geophysical seismic facies classification problems without introducing the subjectivity of human interpreters’ decisions. However, such DL algorithms are “black boxes” by nature, and the underlying basis can be hardly interpreted. Subjectivity is therefore often introduced during the quality control process, and any interpretation of DL models can become an important source of information. To provide a such degree of interpretation and retain a higher level of human intervention, the development and application of explainable DL methods have been explored. To showcase the usefulness of such methods in the field of geoscience, we utilize a prototype-based neural network (NN) for the seismic facies classification problem. The “prototype” vectors, jointly learned to have the stereotypical qualities of a certain label, form a set of representative samples. The interpretable component thereby transforms “black boxes” into “gray boxes.” We demonstrate how prototypes can be used to explain NN methods by directly inspecting key functional components. We describe substantial explanations in three ways of examining: 1) prototypes’ corresponding input–output pairs; 2) the values generated at the specific explainable layer; and 3) the numerical structure of specific shallow layers located between the interpretable latent prototype layer and an output layer. Most importantly, the series of interpretations shows how geophysical knowledge can be used to understand the actual function of the seismic facies classifier and therefore help the DL’s quality control process. The method is applicable to many geoscientific classification problems when in-depth interpretations of NN cl-ssifiers are required.
      PubDate: 2023
      Issue No: Vol. 61 (2023)
       
 
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