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Strong Noise-Tolerance Deep Learning Network for Automatic Microseismic Events Classification IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-27 Jian He, Huailiang Li, Xianguo Tuo, Xiaotao Wen, Wenzheng Rong, Xin He
Identifying useful microseismic events is one of the key steps in monitoring tunnel rockbursts. Here, we propose a strong noise-tolerance deep learning (SNTDL) network for the automatic classification of noisy microseismic events. The training set, validation set, and test set of the SNTDL network consist of 27989 unfiltered microseismic recordings. First, to comprehensively characterize the microseismic
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Seismic Impedance Inversion Based on Residual Attention Network IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-25 Bangyu Wu, Qiao Xie, Baohai Wu
Deep learning (DL) has achieved promising results for impedance inversion via seismic data. Generally, these networks, composed of convolution layers and residual blocks, tend to deliver good results with deep architectures. Nevertheless, deep networks accompany a large number of parameters and longer training time. The volume of seismic data, especially 3-D scenarios, is very large. Therefore, it
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Dynamic Multiscale Fusion Generative Adversarial Network for Radar Image Extrapolation IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-25 Shengchao Chen, Ting Shu, Huan Zhao, Qilin Wan, Jincan Huang, Cailing Li
Typhoons, a kind of devastating natural disaster, have caused incalculable damages worldwide. The meteorological radar image is essential for weather forecasting, especially typhoons. The weather nowcasting (future 0–6 h) can be implemented via extrapolating radar images without using the primary weather forecasting method—the numerical weather prediction model. However, the existing related techniques
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Multimodal Semantic Consistency-Based Fusion Architecture Search for Land Cover Classification IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Xiao Li, Lin Lei, Caiguang Zhang, Gangyao Kuang
Multimodal land cover classification (MLCC) using the optical and synthetic aperture radar (SAR) modalities has resulted in outstanding performances over using only unimodal data due to their complementary information on land properties. Previous multimodal deep learning (MDL) methods have relied on handcrafted multibranch convolutional neural networks (CNN) to extract the features of different modalities
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A Purely Spaceborne Open Source Approach for Regional Bathymetry Mapping IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Nathan Thomas, Brian Lee, Oliver Coutts, Pete Bunting, David Lagomasino, Lola Fatoyinbo
Timely and up-to-date bathymetry maps over large geographical areas have been difficult to create, due to the cost and difficulty of collecting in situ calibration and validation data. Recently, combinations of spaceborne Ice, Cloud, and Elevation Satellite-2 (ICESat-2) lidar data and Landsat/sentinel-2 data have reduced these obstacles. However, to date, there have been no means of automatically extracting
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Sea-Surface Small Floating Target Recurrence Plots FAC Classification Based on CNN IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Yanling Shi, Yaxing Guo, Tingting Yao, Zipeng Liu
In this article, we propose the false-alarm-rate-controllable (FAC) classification of sea clutter recurrence plots (RPs) based on convolutional neural networks (CNNs), which is shortened as RPs-CNN. Sea clutter data are a nonlinear and recursive time series, and RPs provide qualitative analysis of nonlinear and recursive dynamic systems. Thus, we construct the RPs’ datasets to extract the recursive
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Frequency Management System (FMS) for Over-the-Horizon Radar (OTHR) Using a Near-Real-Time Ionospheric Model IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Thayananthan Thayaparan, Hannah Villeneuve, David R. Themens, Benjamin Reid, Michael Warrington, Taylor Cameron, Robyn Fiori
Sky-wave over-the-horizon radar (OTHR) propagates radio waves off the ionosphere to provide long-range surveillance around the Earth’s curvature. Frequency selection for high-latitude and polar OTHRs is challenging unless there is an environmental monitor that addresses the significant ionospheric variability in high-latitude regions, a spectrum monitor that finds unoccupied frequencies in the high-frequency
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Geolocation Error Estimation Method for the Wide Swath Polarized Scanning Atmospheric Corrector Onboard HJ-2 A/B Satellites IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Xuefeng Lei, Zhenhai Liu, Fei Tao, Weizhen Hou, Honglian Huang, Yanqing Xie, Xinxin Zhao, Hao Dong, Peng Zou, Maoxin Song, Zhengqiang Li, Jin Hong
Polarized scanning atmospheric corrector (PSAC) onboard the HuanjingJianzai-2 (HJ-2) A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by nine full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor
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MS-HLMO: Multiscale Histogram of Local Main Orientation for Remote Sensing Image Registration IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Chenzhong Gao, Wei Li, Ran Tao, Qian Du
Multisource image registration is challenging due to intensity, rotation, and scale differences among the images. Considering the characteristics and differences in multisource remote sensing images, a feature-based registration algorithm named multiscale histogram of local main orientation (MS-HLMO) is proposed. Harris corner detection is first adopted to generate feature points. The HLMO feature
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EMRNet: End-to-End Electrical Model Restoration Network IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Zhuo Jia, Yinshuo Li, Wenkai Lu, Ling Zhang, Patrice Monkam
The traditional method to improve the resolution in electromagnetic inversion is increasing the number of iterations, which displays poor nonlinear mapping and strong nonuniqueness. To meet this challenge, a new strategy is proposed via reconstructing the geoelectric model for traditional inversion results through a deep neural network (DNN). DNN possesses the advantage of establishing an uncertain
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Aerosol Optical Depth Retrieval Based on Neural Network Model Using Polarized Scanning Atmospheric Corrector (PSAC) Data IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Zheng Shi, Zhengqiang Li, Weizhen Hou, Linlu Mei, Lin Sun, Chen Jia, Ying Zhang, Kaitao Li, Hua Xu, Zhenhai Liu, Bangyu Ge, Jin Hong, Yanli Qiao
As the successors of the Huanjing Jianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two Huanjing Jianzai-2 (HJ-2) A/B satellites have been successfully launched on September 27, 2020. The polarized scanning atmospheric corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric
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Adaptive Target Extraction Method in Sea Clutter Based on Fractional Fourier Filtering IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Xiaowen Bi, Shenglong Guo, Yunxiu Yang, Qin Shu
Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this article focuses on the direct extraction of the target echo. According to the difference
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Location of Synthetic Aperture Radar Imagery via Range History IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Maoqiang Jing, Guo Zhang
Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote-sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not
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FSODS: A Lightweight Metalearning Method for Few-Shot Object Detection on SAR Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Zheng Zhou, Jie Chen, Zhixiang Huang, Huiyao Wan, Pei Chang, Zhao Li, Baidong Yao, BoCai Wu, Long Sun, Mengdao Xing
At present, few-shot object detection research in the field of optical remote sensing images has been conducted, but few-shot object detection in the field of synthetic aperture radar (SAR) images has rarely been explored. To this end, this article proposes a lightweight metalearning-based SAR image few-shot object detection method, which improves the accuracy and speed of SAR image few-shot object
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Toward Weak Signal Analysis in Hyperspectral Data: An Efficient Unmixing Perspective IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-21 Xiangfei Shen, Haijun Liu, Jian Qin, Fangyuan Ge, Xichuan Zhou
Many unmixing methods hold the assumption that endmembers correspond to major land covers, but not true for some unmixing tasks where observed minor object signals corresponding to some special types of endmembers are relatively weak. When there exist weak signals that have low intensity potentially caused by subtle mixing abundance fractions regarding the endmembers of minor objects, the traditional
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Microwave Photonic SAR High-Precision Imaging Based on Optimal Subaperture Division IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-20 Yu Hai, Zhongyu Li, Junjie Wu, Yuting Li, Yuping Xiao, Wangzhe Li, Ruoming Li, Bingnan Wang, Yulin Huang, Jianyu Yang
Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional
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Multilayer Degradation Representation-Guided Blind Super-Resolution for Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-20 Xudong Kang, Jier Li, Puhong Duan, Fuyan Ma, Shutao Li
Remote sensing image super-resolution (SR) aims to boost the image resolution while recovering rich high-frequency details. Currently, most of the SR methods are based on an assumption that the degradation kernel is a specific downsampler. However, the degradation kernel is unknown and sophisticated for real remote sensing scenes, leading to a severe performance drop. To alleviate this problem, we
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Simultaneous Seismic Data Interpolation and Denoising Based on Nonsubsampled Contourlet Transform Integrating With Two-Step Iterative Log Thresholding Algorithm IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-20 Chao Li, Xiaotao Wen, Xingye Liu, Shaohuan Zu
Seismic data interpolation and denoising play vital roles in obtaining complete and clean data in seismic data processing. Seismic data usually miss along various spatial axes and always mix with random noise. To obtain complete and clean seismic data, reconstruction technology can interpolate missing data and attenuate random noise. A nonsubsampled contourlet transform (NSCT) is an effective transform
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Multiregion Scale-Aware Network for Building Extraction From High-Resolution Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-20 Yu Liu, Zhengyang Zhao, Shanwen Zhang, Lei Huang
Building extraction is an essential task due to its relevance to urban planning and automatic surveying mapping activities. Despite the existing convolutional neural network-based methods that have achieved remarkable progress on building extraction from remote sensing images, the accurate extraction of buildings with extremely large variations of scales and layouts is still challenging. In this study
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Using 4-D Seismic Data for Detecting Gob Areas of Coal Mines: A Case Study From the Zhangji Coal Mine IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-20 Hao Yuan, Jiapeng Liu, Yijun Yuan
Subsidence of the gob area is an important geological problem in coal mines. Such events not only pose potential safety risks, but also can seriously impair the subsequent mining of coal mines. Therefore, detection of gob subsidence is important to ensure sustainable coal mining. Due to the complex seismic and geological conditions in the gob subsidence area, it is difficult to accurately identify
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Bi-Modal Transformer-Based Approach for Visual Question Answering in Remote Sensing Imagery IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-19 Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Mohamed Lamine Mekhalfi, Mansour Abdulaziz Al Zuair, Farid Melgani
Recently, vision-language models based on transformers are gaining popularity for joint modeling of visual and textual modalities. In particular, they show impressive results when transferred to several downstream tasks such as zero and few-shot classification. In this article, we propose a visual question answering (VQA) approach for remote sensing images based on these models. The VQA task attempts
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Dynamic Retrievals From Spaceborne Doppler Radar Measurements: The CConDoR Approach IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-19 Ousmane O. Sy, Simone Tanelli
This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a complex convolution Doppler resampling (CConDoR) formulation, which links the spaceborne pulse-pair (PP) correlation measurements to high-resolution PP products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler
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Ambiguity Clutter Suppression via Pseudorandom Pulse Repetition Interval for Airborne Radar System IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-19 Yukai Kong, Xianxiang Yu, Tao Fan, Guolong Cui, Lingjiang Kong
Ambiguity clutter for airborne radar systems is usually caused by a uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this article proposes an ambiguity clutter suppression method resorting to a pseudorandom pulse repetition interval (PrPRI) waveform. First, an airborne radar clutter model accounting for
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Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-19 Jianqi Chen, Keyan Chen, Hao Chen, Wenyuan Li, Zhengxia Zou, Zhenwei Shi
Fine-grained image classification can be considered as a discriminative learning process where images of different subclasses are separated from each other while the same subclass images are clustered. Most existing methods perform synchronous discriminative learning in their approaches. Although achieving promising results in fine-grained visual classification (FGVC) in natural images, these methods
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LHNet: Laplacian Convolutional Block for Remote Sensing Image Scene Classification IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-19 Wenhua Zhang, Licheng Jiao, Fang Liu, Jia Liu, Zhen Cui
Recently, many state-of-the-art results for remote sensing image scene classification have been achieved by convolutional neural networks (CNNs) due to their large learning capability. However, in the forward process of CNNs, the high-frequency/texture features are gradually blurred with hierarchical downsampling and convolution operations. High-frequency features are important to capture the diversity
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A Novel FDTD-Based 3-D RTM Imaging Method for GPR Working on Dispersive Medium IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-18 Yuxuan Wu, Feng Shen, Minghao Zhang, Yongfei Miao, Tong Wan, Dingjie Xu
Nowadays, benefiting from its strong capability of nondestructive detection, the ground-penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods
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MHANet: A Multiscale Hierarchical Pansharpening Method With Adaptive Optimization IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-18 Dajiang Lei, Jin Huang, Liping Zhang, Weisheng Li
In recent years, the powerful nonlinear modeling capability of convolutional neural networks (CNN) has led to an increasing number of researchers focusing on deep-learning-based pansharpening methods. However, due to the diversity of remote sensing image features and the limitations of the convolution operation, the existing methods are still inadequate in restoring the spatial details of complex remote
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Statistical Sample Selection and Multivariate Knowledge Mining for Lightweight Detectors in Remote Sensing Imagery IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-18 Yiran Yang, Xian Sun, Wenhui Diao, Dongshuo Yin, Zhujun Yang, Xinming Li
In recent years, more concerns are shed on the lightweight detection model in remote sensing (RS), but it is difficult to reach a competitive performance relative to the deep model. Knowledge distillation has been verified as a promising method, which can promote the performance of the lightweight model without extra parameters. While there are two key issues of detection distillation, one is the sample
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Simultaneous Screening and Detection of RFI From Massive SAR Images: A Case Study on European Sentinel-1 IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-18 Ning Li, Hengrui Zhang, Zongsen Lv, Lin Min, Zhengwei Guo
Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to
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A Full-Spectrum Spectral Imaging System Analytical Model With LWIR TES Capability IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Runchen Zhao, Emmett J. Ientilucci
With the popularity of (hyperspectral) remote sensing systems coupled with a myriad of applications comes the need for investigations into hyperspectral system designs and parameter trade-off studies. Analytical models based on statistical descriptions and signal propagation are efficient methods to examine these parameter trade-off studies, as well as sensitivity studies, with low computational cost
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Analysis of Nighttime Radiances Measured by VIIRS Satellite Sensor (NASA/NOAA) Over Coastal Waters at Seasonal and Daily Time Scales. Application to the Observation of River Discharges During Flooding Events IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Malik Chami, Zacharie Aoulad, Sébastien Migeon, Audrey Minghelli
The observation of coastal waters using satellite remote sensing is of primary importance to gain understanding on the complex processes occurring in those highly dynamical ecosystems. The analysis of ocean color data relies on the interaction between the incident sunlight daytime radiation that propagates within the ocean–atmosphere system and the suspended material (hydrosols and aerosols) present
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On the Use of COSMO-SkyMed X-Band SAR for Estimating Snow Water Equivalent in Alpine Areas: A Retrieval Approach Based on Machine Learning and Snow Models IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Emanuele Santi, Ludovica De Gregorio, Simone Pettinato, Giovanni Cuozzo, Alexander Jacob, Claudia Notarnicola, Daniel Günther, Ulrich Strasser, Francesca Cigna, Deodato Tapete, Simonetta Paloscia
This study aims at estimating the dry snow water equivalent (SWE) by using X-band synthetic aperture radar (SAR) data from the COSMO-SkyMed (CSK) satellite constellation. The time series of CSK acquisitions have been collected during the dry snow period in the Alto Adige test site, in the Italian Alps, during the winter seasons from 2013 to 2015 and from 2019 to 2021. The SAR data have been analyzed
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Multiscan Recursive Bayesian Parameter Estimation of Large-Scene Spatial-Temporally Varying Generalized Pareto Distribution Model of Sea Clutter IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Xiang Liang, Han Yu, Peng-Jia Zou, Peng-Lang Shui, Hong-Tao Su
In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter
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Hyperspectral Image Few-Shot Classification Network Based on the Earth Mover’s Distance IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Jiaxing Sun, Xiaobo Shen, Quansen Sun
Deep learning has achieved promising performance in hyperspectral image (HSI) classification. Training deep models usually requires labeling massive HSIs, which, however, is prohibitively time-consuming and expensive. To fill in the gap, this article proposes a novel meta-learning method for HSI few-shot classification that conducts HSI classification with a few labeled samples. Specifically, we introduce
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Oblique In-Scene Atmospheric Compensation IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-15 Daniel S. O’Keefe, Stephen N. Nauyoks, Michael R. Hawks, Joseph Meola, Kevin C. Gross
This research introduces a novel oblique longwave infrared (LWIR) atmospheric compensation (AC) technique for hyperspectral imagery, oblique in-scene AC (OISAC). Current AC algorithms have been developed for nadir-viewing geometries, which assume that every pixel in the scene is affected by the atmosphere in nearly the same manner. However, this assumption is violated in oblique imaging conditions
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Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Yihuai Lou, Shizhen Li, Shengjun Li, Naihao Liu, Bo Zhang
Although there are plenty of approaches proposed for addressing seismic volumetric dip estimation, it still suffers from several limitations, for example, the expensive computation cost, the perturbations from sequence stratigraphic anomalies, and the difficulty for handling the complicated geologic structures. Recently, deep learning (DL)-based models have been proposed for seismic dip estimation
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Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Xingyu Lu, Chenchen Wang, Xiaofeng Xu, Huizhang Yang, Shiyuan Zhang, Ke Tan, Xianglin Bao, Weimin Su, Hong Gu
Since the start of the Sentinel-1 (S-1) mission, numerous cases of severe image degradation caused by radio frequency interference (RFI) have been reported, which puts forward an urgent need for RFI identification and mitigation. In this article, an automatic RFI identification method is proposed based on a Siamese-type deep convolutional neural network (Siam-CNN-RIM). The Siam-CNN-RIM can be served
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Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Kaiyu Zhang, Xiaolei Lv, Huiming Chai, Jingchuan Yao
Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for a few changed areas for traditional change detection methods. In this article, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three
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Transferring CNN With Adaptive Learning for Remote Sensing Scene Classification IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Weiquan Wang, Yushi Chen, Pedram Ghamisi
Accurate classification of remote sensing (RS) images is a perennial topic of interest in the RS community. Recently, transfer learning, especially for fine-tuning pretrained convolutional neural networks (CNNs), has been proposed as a feasible strategy for RS scene classification. However, because the target domain (i.e., the RS images) and the source domain (e.g., ImageNet) are quite different, simply
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Initial Analysis of Spectral Smile Calibration of Hyperspectral Imager Suite (HISUI) Using Atmospheric Absorption Bands IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Satoru Yamamoto, Satoshi Tsuchida, Minoru Urai, Hiroki Mizuochi, Koki Iwao, Akira Iwasaki
This article reports on the initial analysis of spectral smile calibration of the Hyperspectral Imager Suite (HISUI) onboard the International Space Station, which has been continuously acquiring data since September 4, 2020. HISUI is an optical hyperspectral imager consisting of two subsystems: VNIR covering 400–980 nm at intervals of 10 nm and SWIR covering 895–2481 nm at 12.5-nm intervals. Based
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Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-14 Menghui Jiang, Huanfeng Shen, Jie Li
It is a challenging task to integrate the spatial, temporal, and spectral information of multisource remote sensing images, especially in the case of heterogeneous images. To this end, for the first time, this article proposes a heterogeneous integrated framework based on a novel deep residual cycle generative adversarial network (GAN). The proposed network consists of a forward fusion part and a backward
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Enhanced Spatial–Temporal Savitzky–Golay Method for Reconstructing High-Quality NDVI Time Series: Reduced Sensitivity to Quality Flags and Improved Computational Efficiency IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Xue Yang, Jin Chen, Qingfeng Guan, Huan Gao, Wei Xia
The spatial–temporal Savitzky–Golay (STSG) method for noise reduction can address the problem of tempor- ally continuous normalized difference vegetation index (NDVI) gaps and effectively increase local low NDVI values without overcorrection. However, STSG largely depends on the quality flags of the NDVI time-series data, and inaccurate quality flags yield misleading final results. STSG also requires
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Collaboration Between Multiple Experts for Knowledge Adaptation on Multiple Remote Sensing Sources IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Ba Hung Ngo, Ju Hyun Kim, So Jeong Park, Sung In Cho
Due to the unique characteristics of remote sensing (RS) data, it is challenging to collect richer labeled samples for training the deep learning model compared with the natural image data. To solve this problem, recently, multisource-single-target (MS $^{2}\text{T}$ ) scenarios have started receiving significant attention in which the knowledge from multiple sources is integrated to transfer to a
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Radio Frequency Interference Signature Detection in Radar Remote Sensing Image Using Semantic Cognition Enhancement Network IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Mingliang Tao, Jieshuang Li, Junli Chen, Yanyang Liu, Yifei Fan, Jia Su, Ling Wang
Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion and image degradation or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality
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Hyperspectral Estimation of Soil Copper Concentration Based on Improved TabNet Model in the Eastern Junggar Coalfield IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Yuan Wang, Abdugheni Abliz, Hongbing Ma, Li Liu, Alishir Kurban, Ümüt Halik, Matti Pietikäinen, Wenjuan Wang
China is the largest coal consumer in the world. The massive exploitation and utilization of coal resources have resulted in serious problems of heavy metal pollution and environmental contamination, such as soil degradation, water pollution, crop damage, and even threatening human lives. Therefore, monitoring soil heavy metal pollution quickly and in real time is an urgent task at present. This research
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Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Meng Jia, Cheng Zhang, Zhiqiang Zhao, Lei Wang
Detecting land cover change is an essential task in very-high-spatial-resolution (VHR) remote sensing applications. However, because VHR images can capture the details of ground objects, the scenes of VHR images are usually complex. For example, VHR images usually show distinct appearances or features of the same object, aroused by noise, climate conditions, imaging angles, etc. To address this issue
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Tensor Approximation With Low-Rank Representation and Kurtosis Correlation Constraint for Hyperspectral Anomaly Detection IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Zhuang Li, Ye Zhang, Junping Zhang
Anomaly detection is an active topic in hyperspectral image processing. Recently, low-rank representation (LRR)-based approaches have shown satisfactory results in wide anomaly detection applications. However, the existing LRR methods still have the following two problems: 1) setting a fixed value as a termination condition of the iterative constraint often results in the loss of target information
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Self-Supervised Feature Learning for Multimodal Remote Sensing Image Land Cover Classification IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Zhixiang Xue, Xuchu Yu, Anzhu Yu, Bing Liu, Pengqiang Zhang, Shentong Wu
Deep learning models have shown great potential in remote sensing (RS) image processing and analysis. Nevertheless, there are insufficient labeled samples to train deep networks, which seriously affects the performance of these models. To resolve this contradiction, we propose a generative self-supervised feature learning (S2FL) architecture for multimodal RS image land cover classification. Specifically
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Absorption Attenuation Compensation Using an End-to-End Deep Neural Network IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-13 Chen Zhou, Shoudong Wang, Zixu Wang, Wanli Cheng
Absorption attenuation compensation is an important part of seismic data processing. It enhances the resolution of nonstationary seismic data by compensating the amplitude energy and correcting phase distortion. The stabilized inverse $Q$ -filter method, a widely used attenuation compensation method, constructs compensation operators based on stratigraphy-related assumptions and compensates seismic
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Retrieval of Sea Surface Temperature From HY-1B COCTS IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-12 Mingkun Liu, Christopher J. Merchant, Owen Embury, Jianqiang Liu, Qingjun Song, Lei Guan
The Chinese Ocean Color and Temperature Scanner (COCTS) onboard Haiyang-1 (HY-1) series satellites has two thermal infrared channels with the spectrum range of 10.30–11.40 and 11.40– $12.50 \ \mu \text{m}$ for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in
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Hyperspectral Anomaly Detection With Relaxed Collaborative Representation IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-12 Zhaoyue Wu, Hongjun Su, Xuanwen Tao, Lirong Han, Mercedes E. Paoletti, Juan M. Haut, Javier Plaza, Antonio Plaza
Anomaly detection has become an important remote sensing application due to the abundant spectral and spatial information contained in hyperspectral images. Recently, hyperspectral anomaly detection methods based on the collaborative representation (CR) model have attracted significant attention. Nevertheless, these methods have to face two main challenges: 1) all features (spectral signatures) are
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Coarse-Refine Network With Upsampling Techniques and Fourier Loss for the Reconstruction of Missing Seismic Data IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-12 Hanjoon Park, Jun-Woo Lee, Jongha Hwang, Dong-Joo Min
Seismic data are often irregularly or insufficiently sampled along the spatial direction due to malfunctioning of receivers and limited survey budgets. Recently, machine learning techniques have begun to be used to effectively reconstruct missing traces and obtain densely sampled seismic gathers. One of the most widely used machine learning techniques for seismic trace interpolation is UNet with the
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Variable Subpixel Convolution Based Arbitrary-Resolution Hyperspectral Pansharpening IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-11 Lin He, Jinhua Xie, Jun Li, Antonio Plaza, Jocelyn Chanussot, Jiawei Zhu
Standard hyperspectral (HS) pansharpening relies on fusion to enhance low-resolution HS (LRHS) images to the resolution of their matching panchromatic (PAN) images, whose practical implementation is normally under a stipulation of scale invariance of the model across the training phase and the pansharpening phase. By contrast, arbitrary resolution HS (ARHS) pansharpening seeks to pansharpen LRHS images
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Deep Low-Rank Graph Convolutional Subspace Clustering for Hyperspectral Image IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-11 Tianhao Han, Sijie Niu, Xizhan Gao, Wenyue Yu, Na Cui, Jiwen Dong
Deep subspace clustering (DSC) has achieved considerable success in the classification task of hyperspectral images (HSIs) without background (defined as noisy samples) compared with traditional subspace clustering methods. Unfortunately, directly applying DSC to classify land-cover on HSI datasets with background may suffer from the degradation of classification performance. In this article, we propose
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Continuous Human Activity Recognition With Distributed Radar Sensor Networks and CNN–RNN Architectures IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-11 Simin Zhu, Ronny Gerhard Guendel, Alexander Yarovoy, Francesco Fioranelli
Unconstrained human activities recognition with a radar network is considered. A hybrid classifier combining both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for spatial–temporal pattern extraction is proposed. The 2-D CNNs (2D-CNNs) are first applied to the radar data to perform spatial feature extraction on the input spectrograms. Subsequently, gated recurrent units
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Translution-SNet: A Semisupervised Hyperspectral Image Stripe Noise Removal Based on Transformer and CNN IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-11 Chengjun Wang, Miaozhong Xu, Yonghua Jiang, Guo Zhang, Hao Cui, Litao Li, Da Li
Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there
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Deception-Jamming Localization and Suppression via Configuration Optimization for Multistatic SAR IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-07 Wenjing Wang, Junjie Wu, Jifang Pei, Zhichao Sun, Jianyu Yang
Multistatic synthetic aperture radar (SAR) has the characteristics of all-day, all-weather, and high-resolution imaging. It can observe a target from different directions simultaneously to obtain multiangle observation information. However, jamming signals can affect multistatic SAR. When multiple range deception jammers exist in the environment, multiple false targets are generated in the multistatic
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Analysis of the 3-D Evolution Characteristics of Ionospheric Anomalies During a Geomagnetic Storm Through Fusion of GNSS and COSMIC-2 Data IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-07 Yang Wang, Yibin Yao, Jian Kong, Changzhi Zhai, Xuanxi Chen, Lulu Shan
To solve the ill-posed and accuracy problems experienced by global navigation satellite system (GNSS) computerized ionosphere tomography (CIT), this study proposes the use of the ionospheric profile data of COSMIC-2 as the initial scale factor to constrain GNSS data. At present, studies are lacking on long-term data volume statistics and accuracy assessment of COSMIC-2 ionospheric profile products
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On Dropsonde Surface-Adjusted Winds and Their Use for the Stepped Frequency Microwave Radiometer Wind Speed Calibration IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-07 Federica Polverari, Joseph W. Sapp, Marcos Portabella, Ad Stoffelen, Zorana Jelenak, Paul S. Chang
The airborne stepped frequency microwave radiometer (SFMR) provides the measurements of 10-m ocean surface wind speed in high and extreme wind conditions. These winds are calibrated using the surface-adjusted wind estimates from the so-called dropsondes. The surface-adjusted winds are obtained from layer-averaged winds scaled to 10-m altitude to eliminate the local surface variability not associated
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Seismoelectric Wave Propagation Simulation by Combining Poro-Viscoelastic Anisotropic Model With Cole–Cole Depression Model IEEE Trans. Geosci. Remote Sens. (IF 8.125) Pub Date : 2022-07-07 Li Han, Yanju Ji, Wenrui Ye, Shuang Wang, Jun Lin, Xingguo Huang
Considering the viscoelastic anisotropy and electrical depression characteristics of the complex geological media, we introduce the generalized standard linear solid (GSLS) model to describe the relaxation effect of the solid skeleton and the Cole–Cole model to describe the frequency dependence of electric conductivity. The seismoelectric model of the poro-viscoelastic anisotropic medium was constructed