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Front Cover IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
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IEEE Transactions on Geoscience and Remote Sensing publication information IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Presents the table of contents for this issue of the publication.
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TechRxiv: Share Your Preprint Research with the World! IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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IEEE Transactions on Geoscience and Remote Sensing information for authors IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Transactions on Geoscience and Remote Sensing institutional listings IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-12-23
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Front Cover IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
Presents the front cover for this issue of the publication.
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IEEE Transactions on Geoscience and Remote Sensing publication information IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
Presents the table of contents for this issue of the publication.
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Introducing IEEE Collabratec IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
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TechRxiv: Share Your Preprint Research with the World! IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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IEEE Transactions on Geoscience and Remote Sensing information for authors IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Transactions on Geoscience and Remote Sensing institutional listings IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-11-24
Presents the institutional listings for this issue of the publication.
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Front Cover IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
Presents the front cover for this issue of the publication.
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IEEE Transactions on Geoscience and Remote Sensing publication information IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of contents IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
Presents the table of contents for this issue of the publication.
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Introducing IEEE Collabratec IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
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Imagine a community hopeful for the future IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
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IEEE Access IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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IEEE Transactions on Geoscience and Remote Sensing information for authors IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
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IEEE Transactions on Geoscience and Remote Sensing institutional listings IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-07-21
Presents the GRSS society institutional listings.
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Development of a Methodology to Generate In-Orbit Electrooptical Module Temperature-Based Calibration Coefficients for INSAT-3D/3DR Infrared Imager Channels IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-17 Munn Vinayak Shukla; Pradeep K. Thapliyal
It has been observed that the lab-based calibration coefficients of a satellite instrument differ in the actual in-orbit operation due to different environmental conditions. The lab-based coefficients are measured when all the components of the satellite instrument are in thermal equilibrium, while during in-orbit operations, there may be significant variations in temperature between various components
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Hyperspectral Sharpening Approaches Using Satellite Multiplatform Data IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-17 Rocco Restaino; Gemine Vivone; Paolo Addesso; Jocelyn Chanussot
The use of hyperspectral (HS) data is growing over the years, thanks to the very high spectral resolution. However, HS data are still characterized by a spatial resolution that is too low for several applications, thus motivating the design of fusion techniques aimed to sharpen HS images with high spatial resolution data. To reach a significant resolution enhancement, high-resolution images should
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Primal–Dual Optimization Strategy With Total Variation Regularization for Prestack Seismic Image Deblurring IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-17 Bowu Jiang; Wenkai Lu
Seismic image, especially for the prestack image, performs a blurred version of the reflectivity image due to spatial aliasing, poor acquisition aperture, and nonuniform illumination. The blurring effects can be quantified by the point spread function (PSF). We herein adopt an explicit space-variant PSF formula, which can be defined as a sequential application of the modeling and migration operators
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Water Body Detection in High-Resolution SAR Images With Cascaded Fully-Convolutional Network and Variable Focal Loss IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-16 Jinsong Zhang; Mengdao Xing; Guang-Cai Sun; Jianlai Chen; Mengya Li; Yihua Hu; Zheng Bao
The water body detection in high-resolution synthetic aperture radar (SAR) images is a challenging task due to the changing interference caused by multiple imaging conditions and complex land backgrounds. Inspired by the excellent adaptability of deep neural networks (DNNs) and the structured modeling capabilities of probabilistic graphical models, the cascaded fully-convolutional network (CFCN) is
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A Pipeline for 3-D Object Recognition Based on Local Shape Description in Cluttered Scenes IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-12 Wuyong Tao; Xianghong Hua; Kegen Yu; Xijiang Chen; Bufan Zhao
In the last decades, 3-D object recognition has received significant attention. Particularly, in the presence of clutter and occlusion, 3-D object recognition is a challenging task. In this article, we present an object recognition pipeline to identify the objects from cluttered scenes. A highly descriptive, robust, and computationally efficient local shape descriptor (LSD) is first designed to establish
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Depolarized Scattering of Rough Surface With Dielectric Inhomogeneity and Spatial Anisotropy IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-11 Ying Yang; Kun-Shan Chen; Xiaofeng Yang; Zhao-Liang Li; Jiangyuan Zeng
This article presents a new index, polarization-conversion ratio (PCR) to characterize depolarized bistatic scattering from rough surfaces with dielectric inhomogeneity and spatial anisotropy. We then investigate the dependence of PCR on both surface and radar parameters. Numerical results show that the distribution of PCR on the scattering plane varies with the polarization state of the incident wave
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Detection of Metallic Objects in Mineralized Soil Using Magnetic Induction Spectroscopy IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-09 Wouter van Verre; Liam A. Marsh; John L. Davidson; Edward Cheadle; Frank J. W. Podd; Anthony J. Peyton
The detection of small metallic objects buried in mineralized soil poses a challenge for metal detectors, especially when the response from the metallic objects is orders of magnitude below the response from the soil. This article describes a new, handheld, detector system based on magnetic induction spectroscopy (MIS), which can be used to detect buried metallic objects, even in challenging soil conditions
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Evaluation of a Neural Network With Uncertainty for Detection of Ice and Water in SAR Imagery IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-09 Nazanin Asadi; K. Andrea Scott; Alexander S. Komarov; Mark Buehner; David A. Clausi
Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular
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Class-Wise Distribution Adaptation for Unsupervised Classification of Hyperspectral Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-09 Zixu Liu; Li Ma; Qian Du
Class-wise adversarial adaptation networks are investigated for the classification of hyperspectral remote sensing images in this article. By adversarial learning between the feature extractor and the multiple domain discriminators, domain-invariant features are generated. Moreover, a probability-prediction-based maximum mean discrepancy (MMD) method is introduced to the adversarial adaptation network
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Tubal-Sampling: Bridging Tensor and Matrix Completion in 3-D Seismic Data Reconstruction IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-03 Feng Qian; Cangcang Zhang; Lingtian Feng; Cai Lu; Gulan Zhang; Guangmin Hu
The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel structure
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Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification From VHR Imagery IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 ZhiYong Lv; Guangfei Li; Zhenong Jin; Jón Atli Benediktsson; Giles M. Foody
Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user’s accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an
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Algorithm for Automatic Scaling of the F-Layer Using Image Processing of Ionograms IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 Mariano Fagre; Jose A. Prados; Jorge Scandaliaris; Bruno S. Zossi; Miguel A. Cabrera; Rodolfo G. Ezquer; Ana G. Elias
In this article, a method is presented for automatic scaling of the F-layer from ionograms based on an image processing technique for the extraction of curvilinear structures. The algorithm obtains the ordinary and extraordinary traces and determines the F2 critical frequency. The performance was tested using a wide data set of ionograms recorded by the Advanced Ionospheric Sounder/Istituto Nazionale
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Ship Detection in Large-Scale SAR Images Via Spatial Shuffle-Group Enhance Attention IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 Zongyong Cui; Xiaoya Wang; Nengyuan Liu; Zongjie Cao; Jianyu Yang
Ship target detection using large-scale synthetic aperture radar (SAR) images has important application in military and civilian fields. However, ship targets are difficult to distinguish from the surrounding background and many false alarms can occur due to the influence of land area. False alarms always occur with ship target detection because most of the area in large-scale SAR images are treated
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Derivation and Validation of Sensor Brightness Temperatures for Advanced Microwave Sounding Unit-A Instruments IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 Banghua Yan; Khalil Ahmad
In this article, we first present a generalized methodology for deriving sensor brightness temperature sensor data records (SDR) from antenna temperature data records (TDR) applicable for Advanced Microwave Sounding Unit-A (AMSU-A) instruments. It includes corrections for antenna sidelobe contributions, antenna emission, and radiation perturbation due to the difference of Earth radiance in the main
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Hyperspectral Unmixing via Latent Multiheterogeneous Subspace IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 Chunzhi Li; Yonggen Gu; Xiaohua Chen; Yuan Zhang; Lijian Ruan
Blind hyperspectral unmixing (BHU) is an important technology to decompose the mixed hyperspectral image (HSI), which is actually an ill-posed problem. The ill-posedness of the BHU is deteriorated by nonlinearity, endmember variability (EV) and abnormal points, which are considered as three challenging intractable interferences currently. To sidestep the challenges, we present a novel unmixing model
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A Dense Feature Pyramid Network-Based Deep Learning Model for Road Marking Instance Segmentation Using MLS Point Clouds IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-02 Siyun Chen; Zhenxin Zhang; Ruofei Zhong; Liqiang Zhang; Hao Ma; Lirong Liu
Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the
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New Observations From the SWIM Radar On-Board CFOSAT: Instrument Validation and Ocean Wave Measurement Assessment IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-01 Danièle Hauser; Cédric Tourain; Laura Hermozo; D. Alraddawi; L. Aouf; B. Chapron; A. Dalphinet; L. Delaye; M. Dalila; E. Dormy; F. Gouillon; V. Gressani; A. Grouazel; G. Guitton; R. Husson; A. Mironov; A. Mouche; A. Ollivier; L. Oruba; F. Piras; R. Rodriguez Suquet; P. Schippers; C. Tison; Ngan Tran
This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument
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The First Attempt of SAR Visual-Inertial Odometry IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-01 Junbin Liu; Xiaolan Qiu; Chibiao Ding
This article proposes a novel synthetic aperture radar visual-inertial odometry (SAR-VIO) consisting of an SAR and an inertial measurement unit (IMU), which aims to enable the observation platform to complete successfully a continuous observation mission in the context of low-cost demand and lack of enough navigation information. First, we establish the observation models of the SAR in a continuous
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Tunnel Magnetic Resonance Tomography for 2-D Water-Bearing Structures Using Rotating Coil With Separated Loop Configuration IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-01 Qi Wang; Chuandong Jiang; Kai Luo
In tunnel construction, to prevent the occurrence of water inrush, the geological conditions of faults and underground rivers must be determined in advance. As a direct detection method of groundwater, magnetic resonance sounding (MRS) has been applied for the advanced detection of water-related hazards in tunnels and mines recently. However, the results of conventional 1-D MRS cannot correctly reflect
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Elastic Full Waveform Inversion With Angle Decomposition and Wavefield Decoupling IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-06-01 Jingrui Luo; Benfeng Wang; Ru-Shan Wu; Jinghuai Gao
Full waveform inversion (FWI) is a powerful tool to understand the real complicated earth model. As FWI is a highly nonlinear problem and depends strongly on the initial model, how to effectively retrieve the large-scale background model is critical for the success of FWI. For elastic FWI (EFWI), the inversion challenge increases because the P-wave and S-wave are coupled together if no mode separation
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Landmine Detection Using Autoencoders on Multipolarization GPR Volumetric Data IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-29 Paolo Bestagini; Federico Lombardi; Maurizio Lualdi; Francesco Picetti; Stefano Tubaro
Buried landmines and unexploded remnants of war are a constant threat for the population of many countries that have been hit by wars in the past years. The huge amount of casualties has been a strong motivation for the research community toward the development of safe and robust techniques designed for landmine clearance. Nonetheless, being able to detect and localize buried landmines with high precision
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Modeling of Correlated Complex Sea Clutter Using Unsupervised Phase Retrieval IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-29 Liwu Wen; Jinshan Ding; Chao Zhong; Qinghua Guo
The spatially and temporally correlated sea clutter with phase information is valuable for marine radar applications. The major difficulty of coherent sea clutter modeling is the generation of the continuous phases. This article presents a new phase retrieval approach for modeling the correlated complex sea clutter based on unsupervised neural networks. The unsupervised short-term and long-term neural
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Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-29 Yule Duan; Hong Huang; Yuxiao Tang
Sparse representation-based graph embedding methods have been successfully applied to dimensionality reduction (DR) in recent years. However, these approaches usually become problematic in the presence of the hyperspectral image (HSI) that contains complex nonlinear manifold structure. Inspired by recent progress in manifold learning and hypergraph framework, a novel DR method named local constraint-based
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A Novel CNN-Based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-29 Linhao Li; Zhiqiang Zhou; Bo Wang; Lingjuan Miao; Hua Zong
Currently, reliable and accurate ship detection in optical remote sensing images is still challenging. Even the state-of-the-art convolutional neural network (CNN)-based methods cannot obtain very satisfactory results. To more accurately locate the ships in diverse orientations, some recent methods conduct the detection via the rotated bounding box. However, it further increases the difficulty of detection
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Multientity Registration of Point Clouds for Dynamic Objects on Complex Floating Platform Using Object Silhouettes IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-29 Feng Wang; Han Hu; Xuming Ge; Bo Xu; Ruofei Zhong; Yulin Ding; Xiao Xie; Qing Zhu
This article is focused on a challenging topic emerging from the registration of point clouds, specifically the registration of dynamic objects with low overlapping ratio. This problem is especially difficult when the static scanner is installed on a floating platform, and the objects it scans are also floating. These issues make most of the automatic registration methods and software solutions invalid
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Sparse Aperture ISAR Imaging Method Based on Joint Constraints of Sparsity and Low Rank IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-28 Chuangzhan Zeng; Weigang Zhu; Xin Jia; Liu Yang
A new inverse synthetic aperture radar (ISAR) imaging framework is proposed to obtain high cross-range resolution under sparse aperture conditions, which is a challenge when the signal-to-noise ratio is low. Motivated by the sparsity and low rank of target’s 2-D distribution, the imaging problem is converted to the simultaneously sparse and low-rank signal matrix reconstruction problem under multiple
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Residual Spectral–Spatial Attention Network for Hyperspectral Image Classification IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-28 Minghao Zhu; Licheng Jiao; Fang Liu; Shuyuan Yang; Jianing Wang
In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning
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Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-28 Jianwei Zheng; Yuchao Feng; Cong Bai; Jinglin Zhang
Recently, convolution neural network (CNN)-based hyperspectral image (HSI) classification has enjoyed high popularity due to its appealing performance. However, using 2-D or 3-D convolution in a standalone mode may be suboptimal in real applications. On the one hand, the 2-D convolution overlooks the spectral information in extracting feature maps. On the other hand, the 3-D convolution suffers from
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Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-28 Xuelong Li; Yue Yuan; Qi Wang
The fusion of hyperspectral (HS) and multispectral (MS) images designed to obtain high-resolution HS (HRHS) images is a very challenging work. A series of solutions has been proposed in recent years. However, the similarity in the structure of the HS image has not been fully used. In this article, we present a novel HS and MS image-fusion method based on nonlocal low-rank tensor approximation and sparse
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Role of Sampling Design When Predicting Spatially Dependent Ecological Data With Remote Sensing IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-28 Alby D. Rocha; Thomas A. Groen; Andrew K. Skidmore; Louise Willemen
Remote sensing opens opportunities to assess spatial patterns on ecological data for a wide range of ecosystems. This information can be used to more effectively design sampling strategies for fieldwork, either to capture the maximum spatial dependence related to the ecological data or to completely avoid it. The sampling design and the autocorrelation observed in the field will determine whether there
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LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-27 Lei Ding; Hao Tang; Lorenzo Bruzzone
The trade-off between feature representation power and spatial localization accuracy is crucial for the dense classification/semantic segmentation of remote sensing images (RSIs). High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level
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Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-27 Minghua Wang; Qiang Wang; Jocelyn Chanussot; Dan Li
Conventional low-rank (LR)-based hyperspectral image (HSI) denoising models generally convert high-dimensional data into 2-D matrices or just treat this type of data as 3-D tensors. However, these pure LR or tensor low-rank (TLR)-based methods lack flexibility for considering different correlation information from different HSI directions, which leads to the loss of comprehensive structure information
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An Operational Method for Validating the Downward Shortwave Radiation Over Rugged Terrains IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-27 Guangjian Yan; Qing Chu; Yiyi Tong; Xihan Mu; Jianbo Qi; Yingji Zhou; Yanan Liu; Tianxing Wang; Donghui Xie; Wuming Zhang; Kai Yan; Shengbo Chen; Hongmin Zhou
Estimation of downward shortwave radiation (DSR) is of great importance in global energy budget and climatic modeling. Although various algorithms have been proposed, effective validation methods are absent for rugged terrains due to the lack of rigorous methodology and reliable field measurements. We propose a two-step validation method for rugged terrains based on computer simulations. The first
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Theoretical Study on Microwave Scattering Mechanisms of Sea Surfaces Covered With and Without Oil Film for Incidence Angle Smaller Than 30° IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-25 Honglei Zheng; Jie Zhang; Yanmin Zhang; Ali Khenchaf; Yunhua Wang
This article is devoted to investigating the microwave scattering mechanisms of oil-free and oil-covered sea surfaces for an incidence angle smaller than 30° in a backscattering configuration. The Elfouhaily spectrum is used to simulate an oil-free sea surface, whereas the Elfouhaily spectrum combined with the Jenkins damping model is applied to the simulation of an oil-covered sea surface. Then, the
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Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-25 Hao Li; Ruyi Feng; Lizhe Wang; Yanfei Zhong; Liangpei Zhang
Sparse unmixing, as a semisupervised unmixing method, has attracted extensive attention. The process of sparse unmixing involves treating the mixed pixels of hyperspectral imagery as a linear combination of a small number of spectral signatures (endmembers) in a standard spectral library, associated with fractional abundances. Over the past ten years, to achieve a better performance, sparse unmixing
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Coherent GNSS Reflection Signal Processing for High-Precision and High-Resolution Spaceborne Applications IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-25 Yang Wang; Y. Jade Morton
This article presents an adaptive hybrid-tracking (AHT) algorithm designed to process GNSS-R signals with a sufficient coherent component. Coherent GNSS-R signals have the potential to enable high-precision and high-resolution carrier-phase measurements for altimetry, sea-level monitoring, soil-moisture monitoring, flood mapping, snow–water equivalent measurements, and so on. The AHT algorithm incorporates
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Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-25 Xiaotian Zhang; Zhe Jia; Zachary E. Ross; Robert W. Clayton
We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and
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Estimation of Vegetation Structure Parameters From SMAP Radar Intensity Observations IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-22 Thomas Jagdhuber; Carsten Montzka; Carlos Lopez-Martinez; Martin J. Baur; Moritz Link; María Piles; Narendra Narayan Das; François Jonard
In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape ${A}_{P}$ and orientation distribution ${\psi }$ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities ( $| {S}_{\mathrm{ HH}} |^{2}$ , $| {S}_{\mathrm{ HV}} |^{2}$ ,
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A Processing Framework for Tree-Root Reconstruction Using Ground-Penetrating Radar Under Heterogeneous Soil Conditions IEEE Trans. Geosci. Remote Sens. (IF 5.855) Pub Date : 2020-05-22 Abderrahmane Aboudourib; Mohammed Serhir; Dominique Lesselier
Since tree roots are important to ecosystems, particularly in the context of global climate change, better understanding of their organization is necessary. Ground-penetrating radar (GPR) appears a useful tool to that effect. In this contribution, a novel processing procedure to reconstruct 3-D root architectures from GPR data in heterogeneous environments is proposed, involving three main steps: 1)