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Geoscience and Remote Sensing, IEEE Transactions on
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 220  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892
Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • IEEE Transactions on Geoscience and Remote Sensing publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Advertisments.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • 3D Segmentation of Trees Through a Flexible Multiclass Graph Cut Algorithm
    • Authors: Jonathan Williams;Carola-Bibiane Schönlieb;Tom Swinfield;Juheon Lee;Xiaohao Cai;Lan Qie;David A. Coomes;
      Pages: 754 - 776
      Abstract: Developing a robust algorithm for automatic individual tree crown (ITC) detection from airborne laser scanning (ALS) data sets is important for tracking the responses of trees to anthropogenic change. Such approaches allow the size, growth, and mortality of individual trees to be measured, enabling forest carbon stocks and dynamics to be tracked and understood. Many algorithms exist for structurally simple forests, including coniferous forests and plantations. Finding a robust solution for structurally complex, species-rich tropical forests remains a challenge; existing segmentation algorithms often perform less well than simple area-based approaches when estimating plot-level biomass. Here, we describe a multiclass graph cut (MCGC) approach to tree crown delineation. This uses local 3D geometry and density information, alongside knowledge of crown allometries, to segment ITCs from airborne light detection and ranging point clouds. Our approach robustly identifies trees in the top and intermediate layers of the canopy, but cannot recognize small trees. From these 3D crowns, we are able to measure individual tree biomass. Comparing these estimates with those from permanent inventory plots, our algorithm can produce robust estimates of hectare-scale carbon density, demonstrating the power of ITC approaches in monitoring forests. The flexibility of our method to add additional dimensions of information, such as spectral reflectance, make this approach an obvious avenue for future development and extension to other sources of 3D data, such as structure from motion data sets.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Multiple-Regression Model Considering Deformation Information for
           Atmospheric Phase Screen Compensation in Ground-Based SAR
    • Authors: Xiaoxia Zhao;Hengxing Lan;Langping Li;Yixing Zhang;Chaodong Zhou;
      Pages: 777 - 789
      Abstract: The accuracy of ground-based synthetic aperture radar (GB-SAR) interferometry is highly affected by the atmospheric phase screen (APS). A multiple-regression model (MRM) for APS estimation with two parameters (line of sight (LOS) distance and the height difference between the targets and the GB-SAR sensor) was proposed. However, this MRM is inapplicable if the LOS deformation of the observed objects has similar behavior to that of the APS. For example, both the APS and the LOS deformation of observed objects can have an increasing trend along the LOS direction. In this article, an improved MRM (IMRM) that considers the spatial pattern of the LOS deformation of observed objects was proposed. The spatial pattern can be expressed by the location information of the observed objects. The applicability of this improved model was demonstrated in a deformation-measured campaign carried out on a high loess cut slope using 285 images obtained by the GB-SAR sensor during a three-day measurement. The similarity and dissimilarity between the APS and the behavior of the LOS deformation of the slope were illustrated and used to estimate the APS. The accuracy of this IMRM was verified using deformation data measured by a displacement meter. This IMRM will be helpful in APS estimation and compensation in GB-SAR interferometry in case studies where the APS and the LOS deformation of observed objects have similar behavior.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Regression Wavelet Analysis for Near-Lossless Remote Sensing Data
    • Authors: Sara Álvarez-Cortés;Joan Serra-Sagristà;Joan Bartrina-Rapesta;Michael W. Marcellin;
      Pages: 790 - 798
      Abstract: Regression wavelet analysis (RWA) is one of the current state-of-the-art lossless compression techniques for remote sensing data. This article presents the first regression-based near-lossless compression method. It is built upon RWA, a quantizer, and a feedback loop to compensate the quantization error. Our near-lossless RWA (NLRWA) proposal can be followed by any entropy coding technique. Here, the NLRWA is coupled with a bitplane-based coder that supports progressive decoding. This successfully enables gradual quality refinement and lossless and near-lossless recovery. A smart strategy for selecting the NLRWA quantization steps is also included. Experimental results show that the proposed scheme outperforms the state-of-the-art lossless and the near-lossless compression methods in terms of compression ratios and quality retrieval.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A New Methodology of Soil Salinization Degree Classification by
           Probability Neural Network Model Based on Centroid of Fractional Lorenz
           Chaos Self-Synchronization Error Dynamics
    • Authors: Anhong Tian;Chengbiao Fu;Her-Terng Yau;Xiao-Yi Su;Heigang Xiong;
      Pages: 799 - 810
      Abstract: In this article, a new methodology for the centroid variation of chaos self-synchronization error dynamics was used to determine soil salinization degree based on probability neural network (PNN). This was done to overcome the difficulties involved in the handling of a large amount of spectroscopy data, as well as many spectral bands and a low determination rate caused by bands redundancy. The spectral reflectance of saline soils in Xinjiang Uygur Autonomous Region was used as data sources. The results showed that salinization in the area was severe. The proportion of saline, moderately saline, and severely saline soil accounted for 67.3% of the total sample. Fractional-order master/slave chaotic analysis was carried out on the characteristics of soil spectroscopy data with different salinization degrees. The differences between integer order and fractional order of chaotic dynamic error were compared. Simulation results showed that changes in the 0.6 order chaos dynamic error were the most significant and so these were used as PNN input vectors. The PNN model was used to identify the nonlinear hyperspectral signal of soil salinization degrees after chaotic system conversion. The input vector was normalized after insertion into the PNN model input layer and was added to the hidden layer for Gaussian operations. Finally, the hidden layer results were used in the summation layer to calculate the correlation. The verification set classification result was 93.5%. The studies showed that the method proposed in this article could serve as a new way for classifying soil salinization, which has a classification accuracy of 93.5%, and the soil salinization degree can be rapidly determined.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Two-Step Processing Method for Diving-Mode Squint SAR Imaging With
           Subaperture Data
    • Authors: Yi Liang;Yanfeng Dang;Guofei Li;Jianxin Wu;Mengdao Xing;
      Pages: 811 - 825
      Abstract: Due to the existence of vertical velocity in diving-squint synthetic aperture radar (SAR) imaging, the azimuth-shift invariance along the horizontal direction is not satisfied. This will lead to a big approximation error and influence the imaging results when the existing imaging processing methods are directly applied. In order to solve these problems, an equivalent model is first introduced to describe the motion characteristic in the diving-squint mode. By adopting this approach, the diving-squint SAR imaging can be treated as the conventional one, i.e., the height remains unchanged, with the azimuth-shift invariance satisfied along the flight direction. Based on the equivalent model, a two-step processing method for the diving-squint SAR imaging with subaperture data is proposed in this article. First, a time-scaling approach is adopted to obtain the well-focused 2-D image in the slant plane. Since the equivalent model will cause the rotation of the imaging projection plane and introduce the severe distortion in the ground imagery, a rapid geometric correction method based on inverse projection is further performed to get the ground imagery with little distortion through 2-D sinc interpolation. Simulation and real-data results validate the effectiveness of the proposed approach.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From
           Sentinel-2/MSI Imagery
    • Authors: Yuanheng Sun;Qiming Qin;Huazhong Ren;Tianyuan Zhang;Shanshan Chen;
      Pages: 826 - 840
      Abstract: The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Supervised Functional Data Discriminant Analysis for Hyperspectral Image
    • Authors: Zhijing Ye;Jiaqing Chen;Hong Li;Yantao Wei;Guangrun Xiao;Jón Atli Benediktsson;
      Pages: 841 - 851
      Abstract: This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral–spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Estimation of Ionospheric Total Electron Content From a Multi-GNSS Station
           in China
    • Authors: Chengli She;Xinan Yue;Lianhuan Hu;Fengguo Zhang;
      Pages: 852 - 860
      Abstract: Ionospheric total electron content (TEC) is an important parameter in ionospheric researches and applications. However, the determination of the absolute value of TEC can be greatly influenced by the differential code biases (DCBs) estimation. Nowadays, there are more and more Global Navigation Satellite System (GNSS) signals available all over the world, which allow us to solve TEC and DCBs with the hypothesis of local spherical symmetry (LSS) imposed on the dual-frequency observations from only one individual station. For comparison, the results based on the global ionospheric map (GIM) act as a reference in this article. On the one hand, different combinations of Global Positioning System (GPS), GLONASS, and BeiDou Navigation Satellite System (BDS) are considered to illustrate the significance of multi-GNSS observations, with the mixed GPS, GLONASS, and BDS combination performing best when compared to the referenced results. On the other hand, different parameters in LSS condition are taken into account to investigate the suitable geometric constraint of LSS, with the differential longitude, latitude, and epoch suggested to be 3.0°, 0.6°, and 4 min, respectively. Moreover, a group of detailed comparisons from several different stations also show that the combined DCBs and ionospheric TEC derived from our method are compatible with those from the GIM-aided method, especially in the low-latitude area. In summary, with the advantage of the multi-GNSS signals from an individual station, our method can estimate the ionospheric TEC and DCBs independently, which could provide a potential tool in the future real-time applications.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Analytical Models for the Electromagnetic Scattering From Isolated Targets
           in Bistatic Configuration: Geometrical Optics Solution
    • Authors: Alessio Di Simone;Walter Fuscaldo;Leonardo M. Millefiori;Daniele Riccio;Giuseppe Ruello;Paolo Braca;Peter Willett;
      Pages: 861 - 880
      Abstract: In this article, we present a fully analytical model for the evaluation of the electromagnetic (EM) field scattered from a composite target in a generic bistatic configuration. The scenario comprises a rectangular parallelepiped target with smooth dielectric faces lying over a rough background surface, modeled as a stochastic process. The single- and multiple-bounce scattering contributions arising from the target, the rough background, and their interactions have been derived under the Kirchhoff approximation (KA)–geometrical optics (GO) solution. This framework enables the evaluation of the bistatic radar cross section (RCS) of the considered composite target via closed-form expressions. The proposed model exhibits good agreement with the literature results based on accurate and well-established numerical methods. Our analytical model is therefore proposed as a valid alternative to numerical techniques, being able to provide reliable results at a negligible computational burden. Finally, the role of the main scene parameters, i.e., target orientation, surface roughness, and polarization in the bistatic RCS of the target, have been analyzed and discussed.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Multi-Feature Weighted Sparse Graph for SAR Image Analysis
    • Authors: Jing Gu;Licheng Jiao;Fang Liu;Xiangrong Zhang;Xu Tang;Puhua Chen;
      Pages: 881 - 891
      Abstract: Sparse representation (SR) method has the advantages of good category distinguishing performance, noise robustness, and data adaptiveness. In this article, a multi-feature weighted sparse graph (MWSG) is presented for synthetic aperture radar (SAR) image analysis. First, multiple types of features are extracted to fully describe the characteristics of SAR image. Then, multiple SRs of samples in multiple feature spaces are obtained by solving a weighted joint SR model, in which the weight is the Gaussian kernel distance among samples. Moreover, a new fusion mechanism is given to integrate multiple weighted SRs, which aims to eliminate the negative influence of the singular data, so the MWSG is obtained. Afterward, the brief steps of the SAR image segmentation and semisupervised classification based on MWSG are stated. A series of experiments on the simulated and real SAR images shows that the MWSG has better performance than other existing relevant methods.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Volcano-Seismic Transfer Learning and Uncertainty Quantification With
           Bayesian Neural Networks
    • Authors: Angel Bueno;Carmen Benítez;Silvio De Angelis;Alejandro Díaz Moreno;Jesús M. Ibáñez;
      Pages: 892 - 902
      Abstract: Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Metric Learning for Approximation of Microwave Channel Error Covariance:
           Application for Satellite Retrieval of Drizzle and Light Snowfall
    • Authors: Ardeshir Ebtehaj;Christian D. Kummerow;F. Joseph Turk;
      Pages: 903 - 912
      Abstract: Improved microwave retrieval of land and atmospheric state variables requires proper weighting of the information content of radiometric channels through their error covariance matrix. Inspired by recent advances in metric learning techniques, a new framework is proposed for a formal approximation of the channel error covariance. The idea is tested for the detection of precipitation and its phase over oceans, using coincidences of passive/active data from the Global Precipitation Measurement (GPM) and CloudSat satellites. The initial results demonstrate that the presented approach cannot only capture the known laws of radiative transfer equations, but also the surrogate signatures that can arise due to the co-occurrence of precipitation and other radiometrically active land-atmospheric state variables. In particular, the results demonstrate high precision (low error) for the low-frequency channels of 10–37 GHz in the detection of both rain and snowfall over oceans. Using the optimal estimate of the channel error covariance through the multi-frequency ${k}$ -nearest neighbor (kNN) classification approach, without any ancillary data, it is demonstrated that the probability of passive microwave detection of snowfall (0.97) can be higher than that of the rainfall (0.88), when drizzle and light snowfall are the dominant form of precipitation. This improvement is hypothesized to be largely related to the information content of the low-frequency channels of 10–37 GHz that can capture the co-occurrence of snowfall with an increased cloud liquid water content, sea ice, and wind-induced changes of surface emissivity.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Value-Consistent Method for Downscaling SMAP Passive Soil Moisture With
           MODIS Products Using Self-Adaptive Window
    • Authors: Fengping Wen;Wei Zhao;Qunming Wang;Nilda Sánchez;
      Pages: 913 - 924
      Abstract: Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the in-situ SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient ( $R$ ) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m3/m3 at a network level. At station level, the $R$ is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m3/m3. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Wave-Height Mapping From Second-Order Harmonic Peaks of Wide-Beam HF Radar
           Backscatter Spectra
    • Authors: Zhen Tian;Yingwei Tian;Biyang Wen;Sijie Wang;Jiurui Zhao;Weimin Huang;Eric W. Gill;
      Pages: 925 - 937
      Abstract: Compact high-frequency surface wave radar has been widely applied to the measurement of sea surface current, but its accuracy and direction resolution of wave parameter estimation are always limited due to the wide beam of the antenna. In this article, a novel wave-height mapping method based on the second-order harmonic peak (SHP) of radar Doppler spectra is proposed to address this concern. The characteristic of the SHP at the Doppler frequency of $sqrt {2}$ times the Bragg frequency is studied through the theoretical derivation and numerical simulation. A relationship between the ratio ( $R$ ) of the SHP power to the Bragg peak power and significant wave height ( $H_{s}$ ) is derived. Furthermore, the $R$ – $H_{s}$ model is improved by incorporating influences, such as background noise and antenna beamwidth. With this improved model, a wave-height mapping algorithm based on the direction finding technique is presented. This approach enables the significant wave-height map extraction using a broad-beam radar. Finally, wave-height maps obtained at different sea states are depicted and analyzed, and the wave heights appearing on the maps are compared with buoy data over a one-month experiment to verify the validity and robustness of the algorithm. During this period, the significant wave height varies from about 0.5 to 4.5 m, and the radar measured wave heights at different range/distance bins show an overall root-mean-square error (RMSE) of 0.33–0.77 m and a correlation coefficient (CC) of 0.78–0.94, with respect to the buoy measurements.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Processing of Spaceborne Squinted Sliding Spotlight and HRWS TOPS Mode
           Data Using 2-D Baseband Azimuth Scaling
    • Authors: Feng He;Zhen Dong;Yongsheng Zhang;Guanghu Jin;Anxi Yu;
      Pages: 938 - 955
      Abstract: This article presents an efficient 2-D baseband azimuth scaling (2-D BAS) approach for the focusing of data acquired in the spaceborne squinted sliding spotlight and high-resolution wide-swath (HRWS) terrain observation by progressive scan (TOPS) imaging modes. The existing approaches can become inefficient or invalid due to the coexistence of central “absolute” and marginal “relative” squint in the above-mentioned modes. In this article, the signal properties of spaceborne squint synthetic aperture radar (SAR) with the antenna electronically steering in the azimuth dimension are analyzed first, based on which a new parity-decomposition-based range equation (PDRE) is presented to dedicatedly model the range histories of targets illuminated by the rotating beam. A novel 2-D BAS approach is then developed to remove the odd asymmetric part but preserve the even symmetric part of PDRE, which means a “true derotation” for eliminating the effect of both absolute and relative squints caused by the beam rotation. An even-order-multinomial perturbation function is applied and integrated into a range-Doppler-based SAR processing kernel to efficiently compensate the azimuth variation of high-order range cell migration (RCM) and azimuth phase modulation caused by the 2-D BAS. The proposed processor is efficient, since none of the data extension, the postprocessing for resolving focused-domain folding, or the 2-D frequency-domain interpolation at high squint is needed. Simulations with point targets in the squinted sliding spotlight and HRWS TOPS modes are used to validate the developed algorithm.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Object Detection in High-Resolution Panchromatic Images Using Deep Models
           and Spatial Template Matching
    • Authors: Biao Hou;Zhongle Ren;Wei Zhao;Qian Wu;Licheng Jiao;
      Pages: 956 - 970
      Abstract: Automatic object detection from remote sensing images has attracted a significant attention due to its importance in both military and civilian fields. However, the low confidence of the candidates restricts the recognition of potential objects, and the unreasonable predicted boxes result in false positives (FPs). To address these issues, an accurate and fast object detection method called the refined single-shot multibox detector (RSSD) is proposed, consisting of a single-shot multibox detector (SSD), a refined network (RefinedNet), and a class-specific spatial template matching (STM) module. In the training stage, fed with augmented samples in diverse variation, the SSD can efficiently extract multiscale features for object classification and location. Meanwhile, RefinedNet is trained with cropped objects from the training set to further enhance the ability to distinguish each class of objects and the background. Class-specific spatial templates are also constructed from the statistics of objects of each class to provide reliable object templates. During the test phase, RefinedNet improves the confidence of potential objects from the predicted results of SSD and suppresses that of the background, which promotes the detection rate. Furthermore, several grotesque candidates are rejected by the well-designed class-specific spatial templates, thus reducing the false alarm rate. These three parts constitute a monolithic architecture, which contributes to the detection accuracy and maintains the speed. Experiments on high-resolution panchromatic (PAN) images of satellites GaoFen-2 and JiLin-1 demonstrate the effectiveness and efficiency of the proposed modules and the whole framework.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Tree Annotations in LiDAR Data Using Point Densities and Convolutional
           Neural Networks
    • Authors: Ananya Gupta;Jonathan Byrne;David Moloney;Simon Watson;Hujun Yin;
      Pages: 971 - 981
      Abstract: LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an $F_{mathrm{ score}}$ of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Machine Learning System for Precipitation Estimation Using Satellite and
           Ground Radar Network Observations
    • Authors: Haonan Chen;V. Chandrasekar;Robert Cifelli;Pingping Xie;
      Pages: 982 - 994
      Abstract: Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas–Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Efficient Deblending in the PFK Domain Based on Compressive Sensing
    • Authors: Benfeng Wang;Jianhua Geng;
      Pages: 995 - 1003
      Abstract: The blended acquisition can help improve the seismic data quality or enhance the acquisition efficiency. However, the blended seismic data should first be separated for subsequent traditional seismic data processing steps. The signal is coherent in the common receiver domain, and the blending noise shows randomness when the blending operator is constructed using a random time delay series. The seismic data can be characterized sparsely by the curvelet transform which can be used for deblending. However, it has a high computational cost, especially for large-volume seismic data. The spectrum of the seismic data is band-limited with the conjugate symmetry property, and thus the principal frequency components can characterize the signal accurately. The size of the principal frequency components is at least halved. Thus, we propose to implement the curvelet transform on the principal frequency wavenumber (PFK) domain data instead of the time-space (TX) domain data. The size of the PFK domain data is at least halved compared with the TX domain data, which can improve the deblending efficiency reasonably. The related formulae are fully derived and the efficiency enhancement analysis is provided in detail. One synthetic and two field artificially blended data are provided to demonstrate the validity and flexibility of the proposed method in the efficiency improvement and the deblending performance. The separated gathers can be beneficial for subsequent traditional seismic data processing procedures.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Infrared Small Target Detection via Low-Rank Tensor Completion With
           Top-Hat Regularization
    • Authors: Hu Zhu;Shiming Liu;Lizhen Deng;Yansheng Li;Fu Xiao;
      Pages: 1004 - 1016
      Abstract: Infrared small target detection technology is one of the key technologies in the field of computer vision. In recent years, several methods have been proposed for detecting small infrared targets. However, the existing methods are highly sensitive to challenging heterogeneous backgrounds, which are mainly due to: 1) infrared images containing mostly heavy clouds and chaotic sea backgrounds and 2) the inefficiency of utilizing the structural prior knowledge of the target. In this article, we propose a novel approach for infrared small target detection in order to take both the structural prior knowledge of the target and the self-correlation of the background into account. First, we construct a tensor model for the high-dimensional structural characteristics of multiframe infrared images. Second, inspired by the low-rank background and morphological operator, a novel method based on low-rank tensor completion with top-hat regularization is proposed, which integrates low-rank tensor completion and a ring top-hat regularization into our model. Third, a closed solution to the optimization algorithm is given to solve the proposed tensor model. Furthermore, the experimental results from seven real infrared sequences demonstrate the superiority of the proposed small target detection method. Compared with traditional baseline methods, the proposed method can not only achieve an improvement in the signal-to-clutter ratio gain and background suppression factor but also provide a more robust detection model in situations with low false–positive rates.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Wave Height Field Extraction From First-Order Doppler Spectra of a
           Dual-Frequency Wide-Beam High-Frequency Surface Wave Radar
    • Authors: Yingwei Tian;Zhen Tian;Jiurui Zhao;Biyang Wen;Weimin Huang;
      Pages: 1017 - 1029
      Abstract: Ocean wave height measurement using a wide-beam high-frequency surface wave radar (HFSWR) remains challenging due to its poor spatial resolution, which significantly limits the application of such compact systems. In this article, a novel method for wave height field extraction from the first-order Doppler spectra of a dual-frequency wide-beam radar is proposed. A model relating significant wave height to the ratio of the first-order spectral powers associated with two radar frequencies is put forward and studied numerically. Through theoretical analysis and experimental validation, it is confirmed that the first-order Doppler peaks of two radar frequencies have arisen from an approximately same direction of arrival (DOA), and their amplitudes are also affected by a similar wave directional spreading. Hereby, an algorithm combining beamforming and direction finding is developed to determine the spatial distribution of the first-order spectral power ratio and derive the significant wave height field. Finally, experimental results are given to verify the algorithm. The radar-derived wave height field agrees well with that obtained using a numerical wave model. Furthermore, the radar-measured wave heights are compared with the data collected by two buoys at the distances of 12.7 and 73 km, respectively. The comparison shows that the corresponding root-mean-square errors are 0.3 and 0.5 m and the correlation coefficients are 0.85 and 0.88, respectively.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Retrieval Method of Vertical Profiles of Reflectivity for Migratory
           Animals Using Weather Radar
    • Authors: Cheng Hu;Kai Cui;Rui Wang;Teng Long;Shuqing Ma;Kongming Wu;
      Pages: 1030 - 1040
      Abstract: Quantifying the distribution of the aerial organisms is essential for investigating the movement and behavior of migratory animals. This large-scale broad-front migration can be readily detected by weather radars. However, estimating their vertical distribution is still biased due to the vertical variability of the reflectivity in the radar beam. In this article, we establish a weather radar biological observation model and propose a retrieval method to identify the vertical profiles of reflectivity (VPRs) using regularization technique, which can eliminate the estimation bias. The performance of the method is evaluated using different radar antenna patterns and different regularization parameters, and a sensitivity analysis is performed. The improvement of the method is represented by comparing to the direct method. We apply this method to autumn migration cases over the east coast of China; the demonstration results show the potential of this method in the study of migratory animals.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Vessel Azimuth and Course Joint Re-Estimation Method for Compact HFSWR
    • Authors: Weifeng Sun;Weimin Huang;Yonggang Ji;Yongshou Dai;Peng Ren;Peng Zhou;Xianfeng Hao;
      Pages: 1041 - 1051
      Abstract: Small-aperture compact high-frequency surface wave radar (HFSWR) suffers from low azimuth accuracy for target detection due to its wide beamwidth. Multitarget tracking (MTT) algorithms, when applied to the raw target detection data of HFSWR, fail to effectively filter the target azimuths, and thus, resulting in inaccurate target tracks and courses. In this article, a vessel azimuth and course joint re-estimation method by exploring Doppler velocity and the information accumulated from consecutive observations is presented. It begins with applying an MTT algorithm to a measured target states data sequence acquired by HFSWR to establish initial target tracks, from which the measured range, azimuth, and radial velocity data sequences are obtained. Then, the azimuth trend is extracted from the obtained azimuth data sequence as roughly corrected azimuth estimates, with which the target locations are roughly corrected. Subsequently, target speeds and initial courses are estimated based on the roughly corrected location data sequence, followed by a data selection procedure based on proposed control parameter rules to select the qualified data for calculating the projected angles in terms of speed and direction, separately. Eventually, the target azimuth data sequence is further refined using a linear azimuth error model, whose parameters are obtained by minimizing the difference between the projected angles using a constrained optimization method. Experimental results from field data demonstrate that the proposed method can estimate the target azimuths with significantly improved accuracy. The deviations of the corrected target locations are considerably reduced, and the accuracy of course estimation is enhanced.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Some Thoughts on Measuring Earthquake Deformation Using Optical Imagery
    • Authors: Min Huang;Yu Zhou;Lejun Lu;Wenjun Zheng;Peizhen Zhang;
      Pages: 1052 - 1062
      Abstract: Optical imagery has been proven to be an effective tool for measuring earthquake deformation in continental regions since its first application in the 1999 Izmit earthquake. In this article, we compile and analyze all the earthquakes that have been investigated with optical image matching by 2019, based on which we comment on various issues regarding measuring earthquake deformation with optical imagery. New generations of very high-resolution (VHR) data are effective for earthquake studies, but orthorectification of the VHR images is the major source of error, which is often ignored. We found that the displacements derived from the WorldView images strongly correlate with the errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) that was used in orthorectification. Based on the observed correlation between displacements and topography, we propose a new DEM-based method using the Advanced Land Observing Satellite (ALOS) World 3-D DEM to reduce the orthorectification errors. Combining the published optical data of earthquake deformation, we re-analyze the coseismic slip distribution and shallow slip deficit (SSD). The SSD model states that the coseismic slip in many strike-slip earthquakes decreases in magnitude toward the surface, but this model remains arguable because the interferometric synthetic aperture radar (InSAR)-derived slip is usually not well-constrained at shallow depths due to decorrelation. Because optical matching directly measures the surface slip, we re-examine the slip distribution of 11 strike-slip earthquakes and find that the SSD model may primarily be artifacts in the InSAR measurements. It is therefore of great importance to include the optical data in earthquake studies to constrain coseismic slip inversions.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Orbital Path and Spacecraft Attitude Correction for the MODIS Lunar
           Spatial Characterization
    • Authors: Truman Wilson;Amit Angal;Xiaoxiong Xiong;
      Pages: 1063 - 1073
      Abstract: For the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua platforms, regularly scheduled lunar observations using spacecraft roll maneuvers have been used extensively for sensor characterization. While the primary purpose of these observations is for radiometric calibration of the reflective solar bands, they have also been leveraged for a number of other sensor performance assessments, such as the band-to-band spatial registration (BBR) and detector-to-detector spatial registration (DDR). The spatial registration calculations are complicated by the fact that the Moon does not move in a straight path across the sensor field of view (FOV). This path is determined by the relative orbital motion between the spacecraft and the Moon and the instrument attitude error that results from the roll maneuver. In this article, we develop a correction for the MODIS lunar spatial characterization measurements by calculating the predicted path of the Moon across the sensor FOV using spacecraft and lunar ephemeris data to model the relative orbital motion between the spacecraft and the Moon along with spacecraft attitude error data acquired during the roll maneuver. The difference between the measured and predicted positions of the Moon in the MODIS FOV can be used to calculate the BBR and DDR results. Since the predicted path across the FOV will be the same for each band, the BBR results will be minimally affected. However, we will show that the along-scan spread in the DDR can be significantly reduced, which results in a much greater consistency throughout the full mission for both Aqua and Terra MODIS.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Object Tracking in Satellite Videos by Improved Correlation Filters With
           Motion Estimations
    • Authors: Shiyu Xuan;Shengyang Li;Mingfei Han;Xue Wan;Gui-Song Xia;
      Pages: 1074 - 1086
      Abstract: As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth’s surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • N-FINDER for Finding Endmembers in Compressively Sensed Band Domain
    • Authors: Adam Bekit;Chein-I Chang;Bernard Lampe;C. J. Della Porta;Chao-Cheng Wu;
      Pages: 1087 - 1101
      Abstract: N-finder algorithm (N-FINDR) has been widely used for finding endmembers in hyperspectral imagery. Since N-FINDR must find all endmembers simultaneously, it requires exhausting all possible $p$ -endmember combinations among the entire data samples with $p$ being the number of endmembers required to be found. Accordingly, directly implementing N-FINDR is practically infeasible. To mitigate this dilemma, two recently developed algorithms called sequential N-FINDR (SQ N-FINDR) and successive N-FINDR (SC N-FINDR) were developed. However, even such an exhaustive search issue can be resolved numerically, another challenging issue for N-FINDR, which remains unsolved, is spectral dimensionality reduction. Because a $p$ -vertex simplex is embedded in a ( $p-1$ )-dimensional spectral data space, N-FINDR does not require full spectral dimensionality to calculate simplex volume (SV). This article presents a compressive sensing (CS) approach to N-FINDR that can find a $p$ -vertex simplex with the maximal SV by SQ/SC N-FINDR in a compressively sensed band domain (CSBD). In particular, to make this idea work, a new CS-based property called restricted SV property (RSVP) can be shown to be preserved in CSBD via a sensing matrix. It is this property that allows what N-FINDR and SQ/SC N-FINDR can achieve in the original data space (ODS) to be also achieved in CSBD. To further show the utility of SQ/SC N-FINDR in both ODS and CSBD as well as SV preserved by RSVP, a series of experiments are conducted for performance analysis.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Interferometric ISAR Imaging of Maneuvering Targets With Arbitrary
           Three-Antenna Configuration
    • Authors: Jiajia Rong;Yong Wang;Tao Han;
      Pages: 1102 - 1119
      Abstract: In order to compensate for the shortcomings of inverse synthetic aperture radar (ISAR) imaging in target recognition, a three-dimensional (3-D) interferometric ISAR (InISAR) imaging technique is developed. The traditional InISAR imaging with three antennas usually assumes that the baselines are orthogonal to each other and the target moves steadily. However, in practical applications, due to the existence of system error and the unknown moving condition of the noncooperative targets, the baselines used for interference are difficult to be absolutely orthogonal and the movement of the target also tends to be complicated. In this article, a practical maneuvering target 3-D imaging algorithm based on the InISAR of an arbitrary three-antenna configuration is investigated. The main contributions of this article are as follows: 1) abstracted from an actual application scenario, a three-antenna InISAR system model with the baselines being nonorthogonal is established; 2) a joint translational motion compensation strategy, involving a joint accumulated cross correlation method for the envelope alignment and a joint Doppler centroid-tracking approach for the phase correction, is devised for the image coregistration in the range dimension; 3) a complicated movement model of the maneuvering target is described, and a fast high-resolution ISAR imaging algorithm maximum likelihood (ML)-fractional Fourier transform (FRFT), which incorporates the ML with the FRFT, is proposed; and 4) a coordinate correction technique is developed to eliminate the distortion of the 3-D image caused by the nonorthogonality of baselines. The effectiveness and performance of the proposed algorithm are evaluated by several simulations at the last of the article.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A New Baseline Linear Combination Algorithm for Generating Urban Digital
           Elevation Models With Multitemporal InSAR Observations
    • Authors: Hui Luo;Zhenhong Li;Zhen Dong;Peng Liu;Chisheng Wang;Jun Song;
      Pages: 1120 - 1133
      Abstract: The lack of high-resolution digital elevation model (DEM) data presents one major limitation for deformation mapping using synthetic aperture radar interferometry (InSAR) techniques with high-spatial-resolution radar imagery (e.g., TerraSAR-X). This article presents a baseline linear combination (BLC) approach to generate interferograms with nearly zero baselines so as to minimize the effects of the uncertainties in the DEM used. It incorporates the baseline combination (BC) method with adjacent gradient networking to successfully unwrap the interferograms even in abruptly discontinuous areas, which in turn can be used to estimate a high-resolution DEM. The BLC approach does not require any deformation model; instead, it utilizes nearly zero-baseline interferograms to assist with 3-D phase unwrapping. Application of the BLC approach to the TerraSAR-X data set in Shenzhen, China, shows that the BLC-derived DEM agrees with the digital surface model (DSM) obtained from light detection and ranging (LiDAR) with a correlation coefficient of 0.998 and a root-mean-square error (RMSE) of 2.05 m, demonstrating the effectiveness of the BLC approach. Note that the BLC approach is not only able to be employed in urban areas with high buildings but also in mountain areas with steep slopes.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Improved Lunar Intrusion Detection Algorithm for the CrIS Sensor Data
    • Authors: Yong Chen;Denis Tremblay;Likun Wang;Flavio Iturbide-Sanchez;
      Pages: 1134 - 1145
      Abstract: As one of the calibration reference targets used to calibrate the cross-track infrared sounder (CrIS) earth scene (ES) measurements, the stable deep space (DS) reference spectrum in the 30-scan DS calibration moving window is very important for the accuracy of the calibrated ES radiances. The DS view changes when the lunar radiation intrudes into the observation field of view (FOV). In the original CrIS lunar intrusion (LI) detection algorithm implemented in the operational ground processing system, the contaminated DS spectra were not effectively removed from the DS moving window due to large threshold values and the assumption that the first DS spectrum in the moving window was not contaminated. As a result, inaccurate, degraded, or invalid ES radiances were produced in the operational CrIS sensor data record (SDR) during LI events. In this article, an improved LI detection algorithm is developed and implemented into the operational system. First, the new algorithm efficiently finds a contamination-free DS spectrum in the DS 30-scan calibration moving window to use as the reference spectrum. Second, based on the phase characteristics of the complex raw DS spectra during LI events, the LI band-dependent thresholds were derived to effectively reject the contaminated DS spectra and to make the valid DS window size consistent among the three CrIS bands. The new LI algorithm implemented in the operational system shows a successful detection and removal of all the lunar-contaminated DS spectra in the DS moving window, resulting in an improved calibration of ES radiances during LI events.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Diagnosing Emerging Infectious Diseases of Trees Using Ground Penetrating
    • Authors: Iraklis Giannakis;Fabio Tosti;Livia Lantini;Amir M. Alani;
      Pages: 1146 - 1155
      Abstract: Ash dieback, acute oak decline (AOD), and Xylella Fastidiosa are emerging infectious diseases (EIDs) that have spread rapidly in European forests during the last decade. Quarantine measurements have mostly failed to repress the outbreaks and millions of trees have already been infected. Identifying infected trees in a nondestructive manner is of high importance for monitoring, managing, and preventing EIDs. The aim of this article is to examine the capabilities of ground penetrating radar (GPR) on evaluating the internal structure of tree trunks and detecting tree decay associated with EIDs. Traditionally used processing schemes tuned for GPR line acquisitions are modified accordingly to be compatible with the new measurement configurations. In particular, a detection framework is presented based on a modified Kirchhoff and a reverse-time migration. Both of the aforementioned methodologies are compatible with measurements taken along closed irregular curves assuming a homogeneous permittivity distribution. To that extent, prior to migration, a novel focal criterion is used that estimates the bulk permittivity of the host medium from the measured B-scans. The suggested detection scheme is successfully tested on both numerical and laboratory measurements, indicating that GPR has the potential to become a coherent and practical tool for detecting tree decay associated with EIDs.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Unsupervised Manifold-Preserving and Weakly Redundant Band Selection
           Method for Hyperspectral Imagery
    • Authors: Chenhong Sui;Chang Li;Jie Feng;Xiaoguang Mei;
      Pages: 1156 - 1170
      Abstract: Hyperspectral band selection is of great value to alleviate the curse of dimensionality. For many band selection methods, however, the neglect of bandwise usefulness tends to result in the loss of valuable bands, but the retention of useless ones; consequently, this causes deterioration of the classification performance. In this sense, bandwise significance should be emphasized. To address this issue, this article proposes a manifold-preserving and weakly redundant (MPWR) unsupervised band selection method. In the method, a manifold-preserving band-importance metric is put forward to measure the bandwise essentiality. This ensures the retention of bands involving abundant intrinsic structures conductive to classification. Specifically, aimed at obtaining the presented band-importance metric, an attainment algorithm is presented, which mainly relies on the embedding learning and linear regression, followed by the introduction of multi-normalization combination. In addition, concerning the massive redundancy caused by the highly correlated bands, MPWR further establishes a constrained band-weight optimization model. Then, both bandwise manifold-preserving capability and intraband correlation are fully integrated into the band selection process. To solve the problem, a corresponding algorithm within the framework of the alternating direction method of multipliers (ADMM) is also developed. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral data sets. Experimental results demonstrate the superiority and robustness of MPWR.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A New Fusion Algorithm for Depth Images Based on Virtual Views
    • Authors: Jianchen Liu;Linjing Zhang;Zhen Wang;Renli Wang;
      Pages: 1171 - 1181
      Abstract: Common depth image fusion methods use each original image as a reference plane and fuse the depth images using mutual projection. These methods can eliminate inconsistency between the depth images, but they cannot alleviate the point cloud redundancy and computational complexity. This article proposes a virtual view method for depth image fusion, defines a limited number of virtual views by means of view clustering, reduces the redundant calculations, and covers all scenes as much as possible. The depth image is merged ray by ray, and a reliable depth value is obtained via the F-test. Compared with the modified semiglobal matching (TSGM) stereo dense matching algorithm, the accuracy is improved by approximately 50% and the roughness is improved by approximately 50%. Compared with the classic surface reconstruction (SURE) fusion algorithm, there is more fusion depth value in each ray, and the accuracy and roughness are slightly improved. In addition, the algorithm of this article greatly reduces the number of reference planes.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Spatially Explicit Model for Statistical Downscaling of Satellite Passive
           Microwave Soil Moisture
    • Authors: Yaping Xu;Lei Wang;Ziqiang Ma;Bin Li;Rudy Bartels;Cuiling Liu;Xukai Zhang;Jianzhi Dong;
      Pages: 1182 - 1191
      Abstract: We introduce a spatially explicit statistical downscaling (SESD) method that fuses multiscale geospatial data with the soil moisture (SM) product from NASA’s SM Active and Passive (SMAP) satellite. The multiscale data included the 9-km resolution SMAP SM image, 1-km resolution normalized difference vegetation index (NDVI), 1-km digital elevation model (DEM), 1-km resolution MODIS land surface temperature (LST), 500-m resolution gross primary productivity (GPP), 30-m resolution topographical water index (TWI), and West Texas Mesonet (WTM) station data. We used the random forest (RF) machine learning method to make a downscaled SM prediction at the 1-km resolution. Then, a regression kriging was applied to model the unpredicted variability at local scales to produce downscaled SM using the WTM station data. Due to the low number of ground truth samples, the validation was based on Monte -Carlo cross validation (CV) to calculate the unbiased root-mean-square deviation (ubRMSD), root-mean-square deviation (RMSD), and bias of the test set randomly separated from the training set from the WTM station data. Model validation showed that the downscaled SM data at the 1-km resolution can significantly improve the accuracy of the SM product as well as enhancing its spatial resolution. This article has its novelty in using the spatially explicit model to reconcile the scale difference from satellite data and ground observations.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Compound Regularization of Full-Waveform Inversion for Imaging Piecewise
    • Authors: Hossein S. Aghamiry;Ali Gholami;Stéphane Operto;
      Pages: 1192 - 1204
      Abstract: Full-waveform inversion (FWI) is an iterative nonlinear waveform matching procedure, which seeks to reconstruct unknown model parameters from partial waveform measurements. The nonlinear and ill-posed nature of FWI requires sophisticated regularization techniques to solve it. In most applications, the model parameters may be described by physical properties (e.g., wave speeds, density, attenuation, and anisotropy) that are piecewise functions of space. Compound regularizations are, thus, beneficial to capture these different functions by FWI. We consider different implementations of compound regularizations in the wavefield reconstruction inversion (WRI) method, a formulation of FWI that extends its search space and mitigates the so-called cycle skipping pathology. Our hybrid regularizations rely on the Tikhonov and total variation (TV) functionals, from which we build two classes of hybrid regularizers: the first class is simply obtained by a convex combination (CC) of the two functionals, while the second relies on their infimal convolution (IC). In the former class, the model parameters are required to simultaneously satisfy different priors, while in the latter, the model is broken into its basic components, each satisfying a distinct prior (e.g., smooth, piecewise constant, and piecewise linear). We implement these compound regularizations in WRI using the alternating direction method of multipliers (ADMM). Then, we assess our regularized WRI for seismic imaging applications. Using a wide range of subsurface models, we conclude that the compound regularizer based on IC leads to the lowest error in the parameter reconstruction compared to that obtained with the CC counterpart and the Tikhonov and TV regularizers when used independently.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • ISAR Imaging for Low-Earth-Orbit Target Based on Coherent Integrated
           Smoothed Generalized Cubic Phase Function
    • Authors: Yuhan Du;Yicheng Jiang;Yong Wang;Wei Zhou;Zitao Liu;
      Pages: 1205 - 1220
      Abstract: In the high-resolution inverse synthetic aperture radar (ISAR) imaging of low-earth-orbit space targets, the received signal from one range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. The chirp rates and the quadratic chirp rates of the multicomponent CPS need to be estimated with parameter estimation algorithms to obtain the focused ISAR image. In this article, a new parametric range-instantaneous-Doppler ISAR imaging method is proposed, which introduces a new algorithm for parameter estimation of multicomponent CPSs. The cross terms of the generalized cubic phase function (GCPF) are analyzed. By eliminating the cross terms in the ambiguity function domain, the coherent integrated smoothed GCPF (CISGCPF) is proposed to estimate the quadratic chirp rates. Numerical examples are provided to verify the accuracy of the parameter estimation and the effectiveness of the cross-term suppression for CISGCPF in processing multicomponent signals. Simulated and real data imaging results demonstrate the performance of the CISGCPF-based ISAR imaging method. Comparisons with the existing algorithms show that the proposed method is effective in reconstructing focused images with less fake scatterers.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Novel Approach for ISAR Cross-Range Scaling Based on the Multidelay
           Discrete Polynomial- Phase Transform Combined With Keystone Transform
    • Authors: Yong Wang;Xin Huang;Rui Cao;
      Pages: 1221 - 1231
      Abstract: Cross-range scaling is an important procedure for inverse synthetic aperture radar (ISAR) imaging since the cross-range resolution is always unknown due to the noncooperative targets. In fact, the essential problem of scaling is to estimate the rotation velocity of the target from echoes. Considering that most of the scaling methods have limitations on the requirement of some prior knowledge of the target’s motion, difficulties in handling some complicated-structure targets, and low-signal-to-noise ratio (SNR) conditions, we propose a novel scaling approach based on the multidelay discrete polynomial-phase transform (DPT) combined with keystone transform. Different from the second-order-DPT-based method, the proposed method separates the different components on the 2-D spectrum of time and delay time, which avoids the interference of cross terms and enhances the anti-noise ability. The experiments on linear frequency-modulated (LFM) signals, simulated ISAR echoes, and real-measured data verify the effectiveness and robustness of the proposed method.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Multi-Instrument Observations of the Atmospheric and Ionospheric Response
           to the 2013 Sudden Stratospheric Warming Over Eastern Asia Region
    • Authors: Gang Chen;Yaxian Li;Shaodong Zhang;Baiqi Ning;Wanlin Gong;Akimasa Yoshikawa;Kornyanat Hozumi;Takuya Tsugawa;Zhihua Wang;
      Pages: 1232 - 1243
      Abstract: We investigate the atmospheric and ionospheric response to the 2013 sudden stratospheric warming (SSW) by using multiple instruments located in Eastern Asia. Three meteor radars and five ionosondes are used to investigate the mesospheric zonal wind fields and ionospheric parameters of $F$ -layer virtual height ( $h$ ’F), F2-layer peak height ( $h_{m}text{F}_{2}$ ), and critical frequency ( $f_{o}text{F}_{2}$ ) at mid- and low-latitudes (10.7°N to 40.3°N). The vertical total electron content (TEC) data derived from the ground-based global positioning system receiver network are analyzed to study the ionospheric perturbations in the equatorial ionization anomaly (EIA) region. The changes in equatorial electrojet (EEJ) are observed by using the magnetometer data from stations on and off the magnetic equator. The variations of the $h_{m}text{F}_{2}$ at Sanya and EEJ strength presented the semidiurnal pattern with increase/decrease and eastward/westward currents in the morning/afternoon hours. In addition, the EIA crest moved poleward/equatorward in the morning/afternoon. The $f_{o}text{F}_{2}$ showed the most significant enhancements during daytime at Wuhan and Shaoyang but the $f_{o}text{F}_{2}$ at Sanya and Chumphon reduced mildly. Most importantly, based on the time-period wavelet analysis, the diurnal tidal components in the $f_{o}text{F}_{2}$ -/inline-formula> over Beijing, Wuhan, and Sanya seemed similar those in zonal winds and the semidiurnal tides in the low-latitude $h_{m}text{F}_{2}$ showed the similar temporal variations as those in EEJ strength during the later phase of SSW. Therefore, apart from the local tides propagating from lower atmosphere having influence on the mid- and low-latitude ionosphere directly during the early phase, the equatorial fountain effect modulated by the enhanced tides also disturbed the EIA region.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Generalized Tensor Regression for Hyperspectral Image Classification
    • Authors: Jianjun Liu;Zebin Wu;Liang Xiao;Jun Sun;Hong Yan;
      Pages: 1244 - 1258
      Abstract: In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Local Linear Spatial–Spectral Probabilistic Distribution for
           Hyperspectral Image Classification
    • Authors: Hong Huang;Yule Duan;Haibo He;Guangyao Shi;
      Pages: 1259 - 1272
      Abstract: A key challenge in hyperspectral image (HSI) classification is how to effectively utilize the spectral and spatial information of limited labeled training samples in the data set. In this article, a new spatial–spectral combined classification method, termed local linear spatial-spectral probabilistic distribution (LSPD), has been proposed on the basis of local geometric structure and spatial consistency of HSI. LSPD extracts discriminating spatial–spectral information from limited labeled training samples and their spatial–spectral neighbors. Then, it constructs a multiclass probability map by exploiting the local linear representation and spatial information of HSI. Finally, the spatial–spectral weighted reconstruction has been performed on the probability map, and the class of test sample can be predicted by the maximum value of LSPD. LSPD not only exploits spectral information to discover more intrinsic properties of the labeled training data but also utilizes the spatial relationship between samples to effectively improve discriminating power for classification. Experimental results on the Indian Pines, PaviaU, and HoustonU hyperspectral data sets demonstrate that the proposed LSPD method possesses better classification performance by comparing with some state-of-the-art classifiers.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Individual Scatterer Model Learning for Satellite Interferometry
    • Authors: Bas van de Kerkhof;Victor Pankratius;Ling Chang;Rob van Swol;Ramon F. Hanssen;
      Pages: 1273 - 1280
      Abstract: Satellite-based persistent scatterer satellite radar interferometry facilitates the monitoring of deformations of the earth’s surface and objects on it. A challenge in data acquisition is the handling of large numbers of coherent radar scatterers. The behavior of each scatterer is time dependent and is influenced by changes in deformation and other phenomena. Built environments are especially challenging since scatterers may have different signal qualities and deformations may vary significantly among objects. Thus, the estimation of the actual deformation requires a functional model and a stochastic model, both of which are typically unknown per scatterer and observation. Here, we present an approach that models the deformation behavior for each individual scatterer. Our technique is applied in a postprocessing phase following the state-of-the-art interferometric processing of persistent scatterers. This addition significantly improves the interpretation of large data sets by separating the relevant phenomena classes more efficiently. It leverages more information than other methods from individual scatterers, which enhances the quality of the estimation and reduces residuals. Our evaluation shows that this technique can discriminate objects in terms of similar deformation characteristics that are independent of the specific spatial position and temporal complexity. Future applications analyzing large data sets collected by satellite radars will, therefore, drastically benefit from this new capability of extracting categorized types of time series behavior. This contribution will augment traditional spatial and temporal analysis and improve the quality of time-dependent deformation assessments.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Pixel-Level Remote Sensing Image Recognition Based on Bidirectional Word
    • Authors: Hongfeng You;Shengwei Tian;Long Yu;Yalong Lv;
      Pages: 1281 - 1293
      Abstract: In the traditional remote sensing image recognition, the traditional features (e.g., color features and texture features) cannot fully describe complex images, and the relationships between image pixels cannot be captured well. Using a single model or a traditional sequential joint model, it is easy to lose deep features during feature mining. This article proposes a new feature extraction method that uses the word embedding method from natural language processing to generate bidirectional real dense vectors to reflect the contextual relationships between the pixels. A bidirectional independent recurrent neural network (BiIndRNN) is combined with a convolutional neural network (CNN) to improve the sliced recurrent neural network (SRNN) algorithm model, which is then constructed in parallel with graph convolutional networks (GCNs) under an attention mechanism to fully exploit the deep features of images and to capture the semantic information of the context. This model is collectively named an improved SRNN and attention-treated GCN-based parallel (SAGP) model. Experiments conducted on Populus euphratica forests demonstrate that the proposed method outperforms traditional methods in terms of recognition accuracy. The validation done on public data set also proved it.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • In-Flight Validation of the ECOSTRESS, Landsats 7 and 8 Thermal Infrared
           Spectral Channels Using the Lake Tahoe CA/NV and Salton Sea CA Automated
           Validation Sites
    • Authors: Simon J. Hook;Kerry Cawse-Nicholson;Julia Barsi;Robert Radocinski;Glynn C. Hulley;William R. Johnson;Gerardo Rivera;Brian Markham;
      Pages: 1294 - 1302
      Abstract: The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched on June 29, 2018, to the International Space Station (ISS). Landsats 7 and 8 were launched on April 15, 1999 and February 11, 2013, respectively. The thermal channels of all three instruments have been validated at the Lake Tahoe, CA/NV, USA, and Salton Sea, CA, USA, automated validation sites. These sites have been used to validate a large number of thermal infrared radiometers including ASTER, MODIS, and VIIRS. We have validated 41 cloud-free ECOSTRESS scenes acquired between July 29, 2018 and June 23, 2019; 625 cloud-free Landsat 7 scenes acquired between June 30, 1999 and February 7, 2019; and 375 cloud-free Landsat 8 scenes acquired between March 10, 2013 and February 8, 2019. Validation involved propagating ground measurements to equivalent at-sensor (vicarious) values and comparing them to the measurements obtained from the sensor using its on-board calibration (OBC). The overall correlation between the in situ measurements and at-sensor radiance for the thermal channels from all three instruments was excellent with $R^{2}$ of 0.98–0.99 in all the spectral channels. All three instruments were shown to meet or improve on their preflight absolute radiometric accuracy requirement with absolute radiometric values of better than ±1 K at 300 K. All three instruments were also shown to have in-flight noise equivalent delta temperatures which were similar to their preflight values and between 0.1 and 0.3 K depending on the spectral channel.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • EVI Time-Series Breakpoint Detection Using Convolutional Networks for
           Online Deforestation Monitoring in Chaco Forest
    • Authors: Francisco Grings;Esteban Roitberg;Veronica Barraza;
      Pages: 1303 - 1312
      Abstract: The Dry Chaco Forest has the highest absolute deforestation rates of all Argentinian forests (current deforestation rate of 150 000 ha yr−1, 0.85% yr−1). The deforestation process is seen as a breakpoint in the enhanced vegetation index (EVI) time series, associated with the change from a typical forest phenology pattern to something else (e.g., bare soil, pasture, and cropland). Therefore, to monitor this process, a near real-time time-series breakpoint-detection model is needed. In this article, we exploited the 18-year-long MODIS EVI time-series data to train a temporal pattern classification model based on convolutional neural networks. Model architecture parameters (optimizer, number of hidden layers, number of neurons, and so on) were selected using an optimization procedure. The trained model then tries to estimate the probability that a given “time-series segment” corresponds to a deforestation event. The model was validated using in situ data derived from high-resolution images. Results are promising, since the model presents good performance for the validation data set [F1-score = 0.85, ${fpr} = 0.0012$ (of the order of the true deforestation rate), ${tpr} = 0.8$ , for a sample size = $50 times 10^{3}$ ] and average performance in a yearly analysis (F1-score = 0.6, sample size = $1120 times 10^{3}$ ). Model performance was studied using two diagnostic tools: activation maps and model ensemble error estimations. Results show that proposed model presents good extrap-lation capabilities, but its maximum F1-score is bounded by error in the available data set (in particular, mislabeled deforestation events).
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • MEO SAR: System Concepts and Analysis
    • Authors: Jalal Matar;Marc Rodriguez-Cassola;Gerhard Krieger;Paco López-Dekker;Alberto Moreira;
      Pages: 1313 - 1324
      Abstract: Existing microwave remote sensing instruments used for Earth observation face a clear tradeoff between spatial resolution and revisit times at global scales. The typical imaging capabilities of current systems range from daily observations at kilometer-scale resolutions provided by scatterometers to meter-scale resolutions at lower temporal rates (more than ten days) typical of synthetic aperture radars (SARs). A natural way to fill the gap between these two extremes is to use medium-Earth-orbit SAR (MEO-SAR) systems. MEO satellites are deployed at altitudes above the region of low Earth orbits (LEOs), ending at around 2000 km and below the geosynchronous orbits (GEOs) near 35 786 km. MEO SAR shows a clear potential to provide advantages in terms of spatial coverage, downlink visibility, and global temporal revisit times, e.g., providing moderate resolution images (some tens of meters) at daily rates. This article discusses the design tradeoffs of MEO SAR, including sensitivity and orbit selection. The use of these higher orbits opens the door to global coverage in one- to two-day revisit or continental/oceanic coverage with multidaily observations, making MEO SAR very attractive for future scientific missions with specific interferometric and polarimetric capabilities.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • A Modified Generative Adversarial Nets Integrated With Stochastic Approach
           for Realizing Super-Resolution Reservoir Simulation
    • Authors: Feilong Han;Hongbing Zhang;Snehamoy Chatterjee;Qiang Guo;Shulin Wan;
      Pages: 1325 - 1336
      Abstract: Simulations and seismic inversions exhibit good performance in reservoir modeling task for the steady performance of conventional techniques. However, they still hardly meet the high demand of petroleum exploration since the impossibility of reaching high resolution both in vertical and lateral directions. Furthermore, simulations can only provide high-resolution results near loggings, while seismic inversions usually contain band-limited problems. Therefore, we present the modified generative adversarial nets with a decoder (DeGAN) as a novel approach, which is integrated with sequential simulation to realize super-resolution reservoir simulation. Specifically, the proposed method provides a geological model with high vertical resolution and optimized by the Zeoppritz function, introducing logging and seismic data simultaneously. After resampling and warping, DeGAN can be trained by these data sets and supplies a structure to generate high-frequency parts for reconstructing a super-resolution simulation of a subsurface profile. The proposed method presents the three-flow architecture of DeGAN to balance the contributions of three neural network models and utilizes this strategy in an offshore area successfully. By introducing multiple data sets, density experiments demonstrate that this approach can provide density profile with super-resolution for revealing possible thin layers, and the frequency distribution is in accord with loggings. The positive result verifies the effectiveness of this approach for providing a super-resolution simulation to supply a solution to the problem of the band-limited profile in seismic inversion.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • ERAstar: A High-Resolution Ocean Forcing Product
    • Authors: Ana Trindade;Marcos Portabella;Ad Stoffelen;Wenming Lin;Anton Verhoef;
      Pages: 1337 - 1347
      Abstract: To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather pre-iction (NWP) output over the open ocean (about 150 km).
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Nonlocal Tensor-Ring Decomposition for Hyperspectral Image Denoising
    • Authors: Yong Chen;Wei He;Naoto Yokoya;Ting-Zhu Huang;Xi-Le Zhao;
      Pages: 1348 - 1362
      Abstract: Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, nonlocal low-rank tensor approximation-based denoising methods have attracted much attention due to their advantage of being capable of fully exploiting the nonlocal self-similarity and global spectral correlation. Existing nonlocal low-rank tensor approximation methods were mainly based on two common decomposition [Tucker or CANDECOMP/PARAFAC (CP)] methods and achieved the state-of-the-art results, but they are subject to certain issues and do not produce the best approximation for a tensor. For example, the number of parameters for Tucker decomposition increases exponentially according to its dimensions, and CP decomposition cannot better preserve the intrinsic correlation of the HSI. In this article, a novel nonlocal tensor-ring (TR) approximation is proposed for HSI denoising by using TR decomposition to explore the nonlocal self-similarity and global spectral correlation simultaneously. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted third-order tensors, which has strong ability to explore these two intrinsic priors and to improve the HSI denoising results. Moreover, an efficient proximal alternating minimization algorithm is developed to optimize the proposed TR decomposition model efficiently. Extensive experiments on three simulated data sets under several noise levels and two real data sets verify that the proposed TR model provides better HSI denoising results than several state-of-the-art methods in terms of quantitative and visual performance evaluations.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Measurement of Coordinates for a Cylindrical Target Using Times of Flight
           from a 1-Transmitter and 4-Receiver UWB Antenna System
    • Authors: Gennadiy P. Pochanin;Lorenzo Capineri;Timothy D. Bechtel;P. Falorni;Giovanni Borgioli;Vadym P. Ruban;Oleksandr A. Orlenko;Tetiana M. Ogurtsova;Oleksandr G. Pochanin;Fronefield Crawford;P. Kholod;L. Bossi;
      Pages: 1363 - 1372
      Abstract: This article presents a new UWB impulse radar system consisting of a central radiating antenna (1.9-GHz center frequency, 2-GHz bandwidth) and four receiving antennas designed for the detection and location of dielectrically large objects with dimensions comparable to the spatial dimensions of the probe pulse. Together with the radar system, a solution method for determining the coordinates for detected targets is developed based on the time of flight (TOF) of the probing pulse along raypaths from the radiating antenna to the object, and then reflected to each of the receiving antennas. An algorithm based on the Pearson’s correlation coefficient is used to accurately determine the TOF of the signals scattered by the object. The antenna geometry makes it possible to use simple trigonometry and Heron’s formula, to calculate the coordinates of the reflecting bright spot on a target. The algorithm has been tested by numerical simulations and experiments with a cylindrical metallic object (diameter 10 cm) and a plastic-cased PMN-2 landmine buried in natural clay soil. For the experiment, GPR signals were acquired on a $4 times 4$ square grid at 10-cm step from a height of about 30 cm above the ground. The system detected the test object in all positions and the positioning error in majority is equivalent to the object size.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Mapping Precipitable Water Vapor Time Series From Sentinel-1
           Interferometric SAR
    • Authors: Pedro Mateus;João Catalão;Giovanni Nico;Pedro Benevides;
      Pages: 1373 - 1379
      Abstract: In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Automatic First Arrival Picking via Deep Learning With Human Interactive
    • Authors: Kuo Chun Tsai;Wenyi Hu;Xuqing Wu;Jiefu Chen;Zhu Han;
      Pages: 1380 - 1391
      Abstract: First break picking is an inevitable process in land seismic data processing, which involves a huge amount of human labor to perform. Even after decades of investigation on the first break picking process, there are still enormous challenges in developing a robust automatic approach. Although many experts proposed techniques to solve the first break picking problems automatically, there are no solid solutions to avoid human labors during the picking process. In the late 20th century, the rise of the artificial intelligence and the advancement of computer hardware have overcome some challenges in first break picking but the level of their success is limited. In this article, we proposed a deep machine learning model to achieve automatic seismic first break picking. Our proposed model can find the underlying factors and determine the first break curve. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies on-site and update the model through active learning. Unfortunately, training the machine learning model on a huge data set that contains unnecessary data points is an inefficient way for both model learning process and human labeling labors. Therefore, training the model with data selected by the experts can highly reduce the training time and the number of data that human has to label. In simulation, we show the advantage of our proposed deep semisupervised neural network, which uses both labeled and unlabeled data sets to achieve higher accuracy compared with the supervised neural networks.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Ice Sheets and Fresh Water Reservoirs as Natural Dielectric Resonators
    • Authors: Alexander G. Voronovich;Scott W. Abbott;Paul E. Johnston;Richard J. Lataitis;Jesse L. Leach;Robert J. Zamora;
      Pages: 1392 - 1397
      Abstract: One proxy for global climate change is the change in the total mass of the Greenland and Antarctic ice sheets. Several complementary techniques have been used to estimate these changes with varying degrees of success. In this article, we describe a new approach that relies on the resonant behavior of ice masses. For very low electromagnetic (EM) frequencies (i.e., ≤2 kHz), pure ice acts like a strong dielectric resonator. Resonances can be excited in ice sheets by ambient EM noise from, for example, distant thunderstorms. The EM frequency spectrum measured in the vicinity of the ice mass should exhibit corresponding resonant frequencies. The evolution of the resonant modes over time can be used to monitor changes in ice mass and shape. The same approach can be, in principle, applied to monitor changes in the volume of fresh water reservoirs.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Impact of Compressive Stress on Microwave Dielectric Properties of
           Feldspar Specimen
    • Authors: Wenfei Mao;Lixin Wu;Yuan Qi;
      Pages: 1398 - 1408
      Abstract: As one of the most important electrical properties, the dielectric permittivity of minerals and rocks has been studied with respect to its influencing factors, such as texture, density, moisture, frequency, pressure, etc., and has aroused great interest in geophysics, mineralogy, petrology, and microwave remote sensing. However, the studies on the effect of stress on the dielectric property of rock are limited to lower frequencies, and the related mechanism is not yet clear. Considering the limitations of traditional testing methods and the complexities of compositions and structures of rock specimens, in this article, we choose the minerals of the feldspar group as specimens to investigate the impact of compressive stress on their dielectric property at a higher frequency range from 2 to 18.3 GHz. For this purpose, an open coaxial resonator probe is applied to measuring the alteration of the dielectric permittivity of the feldspar specimen in the process of increasing compressive stress using a specially designed loading device. The dielectric constants of the feldspar specimen at all of the five high-frequency points demonstrate obviously decreasing trends with increasing compressive stress. The particular discovery is that the ionic and electronic polarizations are much sensitive to the increasing compressive stress in the microwave frequency band, where the minerals behave like ionic crystals. This article implies that the variation of the microwave dielectric permittivity of rock mass under altering crustal stress due to tectonic plate movements and engineering disturbances is an important factor to be considered in applying radar investigation and microwave remote sensing for mineral exploration, geological exploration, and geo-hazard perception.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Physical and Biological Satellite Observations of the Northwest African
           Upwelling: Spatial Extent and Dynamics
    • Authors: Anass El Aouni;Véronique Garçon;Joël Sudre;Hussein Yahia;Khalid Daoudi;Khalid Minaoui;
      Pages: 1409 - 1421
      Abstract: The region along the North-West African coast (20°N to 36°N and 4°W to 19°W) is characterized by a persistent and variable upwelling phenomenon almost all year round. In this article, the upwelling features are investigated using an algorithm dedicated to delimit the upwelling area from thermal and biological satellite observations. This method has been developed specifically for sea-surface temperature (SST) images, since they present a high latitudinal variation, which is not present in chlorophyll-a concentration images. Developing on the proposed approach, the spatial and temporal variations of the main physical and biological upwelling patterns are studied. Moreover, a study on the upwelling dynamics, which explores the interplay between the upwelling spatiotemporal extents and intensity, is presented, based on a 14-year time archive of weekly SST and chlorophyll-a concentration data.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Deep Metric Learning-Based Feature Embedding for Hyperspectral Image
    • Authors: Bin Deng;Sen Jia;Daming Shi;
      Pages: 1422 - 1435
      Abstract: Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples. In this case, the proposed model can learn well to compare whether two samples belong to the same class. In another task, when an HSI image (target scene) that needs to be classified is not labeled at all, the embedding model can learn from another similar HSI image (source scene) with sufficient labeled samples and then transfer to the target model by using an unsupervised domain adaptation technique, which not only employs the adversarial approach to make the embedding features from the source and target samples indistinguishable but also encourages the target scene’s embeddings to form similar clusters with the source scene one. After the domain adaptation between the HSIs of the two scenes is finished, any traditional HSI classifier can be used. In a simple manner, the nearest neighbor (NN) algorithm is selected as the classifier for the classification tasks throughout this article. The experimental results from a series of popular HSIs demonstrate the advantages of the proposed model both in the same- and cross-scene classification tasks.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Blind Compensation of Angle Jitter for Satellite-Based Ground-Imaging
    • Authors: Ethan Phelps;Charles A. Primmerman;
      Pages: 1436 - 1449
      Abstract: Space-based ground-imaging lidar has become increasingly feasible with recent technological advances. Compact fiber-optic lasers and single-photon-sensitive Geiger-mode detector arrays push designs toward low pulse energies and high pulse rates. A challenge in implementing such a system is imperfect pointing knowledge caused by angular jitter, exacerbated by long distances between satellite and ground. Without mitigation, angular jitter would cause significant blurring of the 3-D data products. Reducing the error in pointing knowledge to avoid such problems might require extreme mechanical isolation, advanced inertial measurement units (IMUs), star trackers, or auxiliary passive optical sensors. These mitigations can increase cost and size, weight, and power considerably. An alternative approach is demonstrated, in which the two-axis jitter time series is estimated using only the lidar data. Simultaneously, a single-surface model of the ground is estimated as nuisance parameters. Expectation–maximization is used to separate signal and background detections while maximizing the joint posterior probability density of the jitter and surface states. The resulting estimated jitter, when used in coincidence processing or image reconstruction, can reduce the blurring effect of jitter to an amount comparable to the optical diffraction limit.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Estimation of Wind Direction in Tropical Cyclones Using C-Band
           Dual-Polarization Synthetic Aperture Radar
    • Authors: Shengren Fan;Biao Zhang;Alexis A. Mouche;William Perrie;Jun A. Zhang;Guosheng Zhang;
      Pages: 1450 - 1462
      Abstract: Under extreme weather conditions, the imprints of kilometer-scale marine atmospheric boundary layer roll vortices on the ocean surface are clearly visible in synthetic aperture radar (SAR) images of storms. Therefore, information about wind direction in storms can be obtained by analyzing SAR image features caused by boundary layer rolls. VH-polarized SAR imagery captures the structural features of storms well and shows prominent image gradients along the radial directions of the storm. The signal-to-noise ratios of VH-polarized images are small in low wind speed areas, but they are large in the same regions of VV-polarized images. Also, the capability of retrieving the atmospheric rolls orientation in VV-polarization is found to be sensitive to incidence angle, with better performances for larger incidence angles. Thus, there is the potential to retrieve the storm’s wind directions using a combination of the VH- and VV-polarized SAR observations. In this article, we use the local gradient method to estimate tropical cyclone (TC) wind directions from C-band RADARSAT-2 and Sentinel-1A dual-polarization (VV + VH) SAR imagery. As a case study, wind directions with a spatial resolution of 25 km are derived by using both wide-swath VV- and VH-polarized SAR imagery over two hurricanes (Earl and Bertha) and one Typhoon (Meranti). We compare wind directions derived from ten dual-polarization SAR images with collocated wind directions from buoys, Global Positioning System (GPS) dropsondes, scatterometer, and radiometer. Statistical comparisons show that the wind direction bias and root-mean-square error are, respectively, −0.54° and 14.78° for VV-polarization, 0.38° and 14.25° for VH-polarization, 0.20° and 13.30° for VV- and VH-polarization, suggesting dual-polarization SAR is more suitable for the estima-ion of TC wind directions than VV- or VH-polarization SAR.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • SRUN: Spectral Regularized Unsupervised Networks for Hyperspectral Target
    • Authors: Weiying Xie;Jian Yang;Jie Lei;Yunsong Li;Qian Du;Gang He;
      Pages: 1463 - 1474
      Abstract: The high dimensionality of a hyperspectral image (HSI) provides the possibility of deeply capturing the underlying and intrinsic characteristics in spectra, such that targets embedded in the background can be detected. However, redundant information, deteriorated bands, and other interferences from background challenge the target detection problem. In this article, an effective feature extraction method based on unsupervised networks is proposed to mine intrinsic properties underlying HSIs. Our approach, called spectral regularized unsupervised networks (SRUN), imposes spectral regularization on autoencoder (AE) and variational AE (VAE) to emphasize spectral consistency, which is more suitable for characterizing spectral information of HSIs by hidden nodes than the original AE and VAE models. Then, we conduct a simple feature selection algorithm on the hidden nodes in the deepest code to select specific nodes that contain distinguishability between target and background, which is based on the spectral angular difference between a known target spectrum and spectra of other pixels in input. The selected nodes are further weighted adaptively to obtain a discriminative map depending on the observation that each selected node provides different contribution rates to target detection. Experimental results on several data sets illustrate that the proposed SRUN-based target detection algorithm is suitable for targets at the subpixel level and those with structural information.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Unsupervised Multiregion Partitioning of Fully Polarimetric SAR Images
           With Advanced Fuzzy Active Contours
    • Authors: Shiyu Luo;Kamal Sarabandi;Ling Tong;Sen Guo;
      Pages: 1475 - 1486
      Abstract: This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Convolutional Neural Network for Convective Storm Nowcasting Using 3-D
           Doppler Weather Radar Data
    • Authors: Lei Han;Juanzhen Sun;Wei Zhang;
      Pages: 1487 - 1495
      Abstract: Convective storms are one of the severe weather hazards found during the warm season. Doppler weather radar is the only operational instrument that can frequently sample the detailed structure of convective storm which has a small spatial scale and short lifetime. For the challenging task of short-term convective storm forecasting (i.e., nowcasting), 3-D radar images contain information about the processes in convective storm. However, effectively extracting such information from multisource raw data has been problematic due to a lack of methodology and computation limitations. Recent advancements in deep learning techniques and graphics processing units (GPUs) now make it possible. This article investigates the feasibility and performance of an end-to-end deep learning nowcasting method. The nowcasting problem was transformed into a classification problem first, and then, a deep learning method that uses a convolutional neural network (CNN) was presented to make predictions. On the first layer of CNN, a cross-channel 3-D convolution was proposed to fuse 3-D raw data. The CNN method eliminates the handcrafted feature engineering, i.e., the process of using domain knowledge of the data to manually design features. Operationally produced historical data of the Beijing–Tianjin–Hebei region in China was used to train the nowcasting system and evaluate its performance; 3 737 332 samples were collected in the training data set. The experimental results show that the deep learning method improves nowcasting skills compared with traditional machine learning methods.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Imagine a community hopeful for the future
    • Pages: 1496 - 1496
      Abstract: Advertisment, IEEE.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • Introducing IEEE Collabratec
    • Pages: 1497 - 1497
      Abstract: Advertisment, IEEE.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
  • IEEE Access
    • Pages: 1498 - 1498
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Feb. 2020
      Issue No: Vol. 58, No. 2 (2020)
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