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Scanning the Issue Proc. IEEE (IF 20.6) Pub Date : 2023-11-20
Deep-Learning-Based 3-D Surface Reconstruction—A Survey
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Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial Proc. IEEE (IF 20.6) Pub Date : 2023-11-20 Onel L. A. López, Nurul H. Mahmood, Mohammad Shehab, Hirley Alves, Osmel Martínez Rosabal, Leatile Marata, Matti Latva-Aho
Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior
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Lattice Spring Model for Irregular Interface Based on an Adaptive Location Strategy IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Jinxuan Tang, Muming Xia, Hui Zhou, Chuntao Jiang, Jinxin Zheng
The lattice spring model (LSM) is a novel method to simulate seismic wave propagation from a micromechanical perspective, which describes the elastic dynamics in complex media comprehensively and delineates the dynamic characteristics of the wavefield; however, it remains a great challenge to simulate wavefields at irregular interfaces with high accuracy. This work presents an adaptive location LSM
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UHD Aerial Photograph Categorization by Leveraging Deep Multiattribute Matrix Factorization IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-17 Luming Zhang, Guifeng Wang, Zhiming Wang, Yinfu Feng, Bing Tu
There are thousands of observation satellites orbiting the Earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photographs are with ultrahigh definitions (UHDs) and may contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the multiple categories of a rich variety of UHD aerial photographs
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A Novel Cross-Attention Fusion-Based Joint Training Framework for Robust Underwater Acoustic Signal Recognition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-16 Aolong Zhou, Xiaoyong Li, Wen Zhang, Dawei Li, Kefeng Deng, Kaijun Ren, Junqiang Song
Underwater acoustic signal recognition (UASR) systems face challenges in achieving high accuracy when processing complex data with low signal-to-noise ratio (SNR) in underwater environments, leading to limited noise robustness. Conventional approaches typically employ pre-trained denoising models for preprocessing noisy signals. However, due to disparate optimization goals between denoising and recognition
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Extraction of Wheat Spike Phenotypes From Field-Collected Lidar Data and Exploration of Their Relationships With Wheat Yield IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-16 Zhonghua Liu, Shichao Jin, Xiaoqiang Liu, Qiuli Yang, Qing Li, Jingrong Zang, Zhaofeng Li, Tianyu Hu, Zifeng Guo, Jin Wu, Dong Jiang, Yanjun Su
Exploring the relationship between spike phenotypes and wheat yield is crucial for selecting wheat ideotypes, but remains a subject of ongoing debate, primarily due to the lack of efficient spike phenotyping methods, particularly in field environments with complex light conditions. Light detection and ranging (lidar) can precisely capture 3-D plant information, minimally affected by light conditions
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Solving Seismic Wave Equations on Variable Velocity Models With Fourier Neural Operator IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-16 Bian Li, Hanchen Wang, Shihang Feng, Xiu Yang, Youzuo Lin
In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning (DL) enables solving partial differential equations (PDEs), including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous
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AVO Inversion for Low-Frequency Component of the Model Parameters Based on Dual-Channel Convolutional Network IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-16 Qianhao Sun, Zhaoyun Zong
Amplitude variation with offset (AVO) inversion is an important method for estimating elastic parameters in geosciences. The inversion results are highly affected by the initial low-frequency model. Deep learning, as a data mining algorithm, has the potential to capture more reliable low-frequency components from seismic data and well logs. In order to fully utilize limited seismic data and recover
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Onboard Data Management Approach Based on a Discrete Grid System for Multi-UAV Cooperative Image Localization IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-15 Yi Lei, Xiaochong Tong, Chunping Qiu, Yuekun Sun, Jiayi Tang, Congzhou Guo, He Li
Onboard image data management and sharing are the foundations for achieving cooperative data processing and analysis in multiple unmanned aerial vehicles (multi-UAVs). However, various challenges, such as the lack of efficient onboard data indices, restrict the development of multi-UAV cooperative applications. Here, we propose a novel and versatile cooperative data management framework based on a
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Backpack LiDAR-Based SLAM With Multiple Ground Constraints for Multistory Indoor Mapping IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-15 Baoding Zhou, Haoquan Mo, Shengjun Tang, Xing Zhang, Qingquan Li
High-quality 3-D point cloud maps are essential for precise indoor environment modeling. However, constructing such maps in multistory indoor environments is challenging due to the presence of narrow nonstructural spaces, such as staircases, corners, and corridors with similar textures. Simultaneous localization and mapping (SLAM) in these scenes is particularly difficult, as cumulative errors can
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Hyperspectral Image Classification Using Geometric Spatial–Spectral Feature Integration: A Class Incremental Learning Approach IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-15 Jing Bai, Ruotong Liu, Haisheng Zhao, Zhu Xiao, Zheng Chen, Wei Shi, Yong Xiong, Licheng Jiao
Hyperspectral image classification (HSIC) has attracted widespread attention due to its important application in environment alterations and geophysical disaster monitoring. However, surface cultivation is not static as time passes, which leads to different hyperspectral image (HSI) information collected from the same area at different time periods. Therefore, researchers are currently eager to construct
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Transformation-Invariant Network for Few-Shot Object Detection in Remote-Sensing Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-14 Nanqing Liu, Xun Xu, Turgay Celik, Zongxin Gan, Heng-Chao Li
Object detection in remote-sensing images (RSIs) relies on a large amount of labeled data for training. However, the increasing number of new categories and class imbalance make exhaustive annotation impractical. Few-shot object detection (FSOD) addresses this issue by leveraging meta-learning on seen base classes and fine-tuning on novel classes with limited labeled samples. Nonetheless, the substantial
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Efficient BiSAR PFA Wavefront Curvature Compensation for Arbitrary Radar Flight Trajectories IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-14 Tianyue Shi, Xinhua Mao, Andreas Jakobsson, Yanqi Liu
The polar format algorithm (PFA) is a popular choice for general bistatic synthetic aperture radar (BiSAR) imaging due to its computational efficiency and adaptability to situations with complicated geometries or arbitrary flight trajectories. However, efficient and accurate compensation of 2-D residual phase errors induced by the wavefront curvature remains challenging when obtaining high-quality
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GSGNet-S*: Graph Semantic Guidance Network via Knowledge Distillation for Optical Remote Sensing Image Scene Analysis IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Wujie Zhou, Yangzhen Li, Juan Huang, Weiqing Yan, Meixin Fang, Qiuping Jiang
In recent years, optical remote sensing image (ORSI) scene analysis has attracted increasing interest. However, existing networks show a trend of bifurcation. Lightweight networks have very high inference speed but poor inference of contextual information in highly complex backgrounds. In contrast, networks with high-performance contextual information reasoning capability require many parameters and
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Make Segment Anything Model Perfect on Shadow Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Xiao-Diao Chen, Wen Wu, Wenya Yang, Hongshuai Qin, Xiantao Wu, Xiaoyang Mao
Compared to models pretrained on ImageNet, the segment anything model (SAM) has been trained on a massive segmentation corpus, excelling in both generalization ability and boundary localization. However, these strengths are still insufficient to enhance shadow detection without additional training, and it raises the question: do we still need precise manual annotations to fine-tune SAM for high detection
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A Novel Tropospheric Error Formula for Ground-Based GNSS Interferometric Reflectometry IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Peng Feng, Rüdiger Haas, Gunnar Elgered
We deduce a novel interferometric tropospheric error (NITE) formula for ground-based global navigation satellite system interferometric reflectometry (GNSS-IR). This formula contains two parts: a geometric displacement error that accounts for the reflection point change due to the atmosphere and Earth’s curvature, and a path delay derived following the definition of the mapping function (with the small
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Spatial Resolution Enhancement of HY-2B Scanning Microwave Radiometer Low-Frequency Data IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Mingyao He, Xiaobin Yin, Yan Li, Qing Xu, Wu Zhou, Mingsen Lin, Shishuai Wang, Mutao Liu, Yidi Wei
Microwave radiometers are widely used in Earth observation and ocean monitoring for their strong penetration ability. Nevertheless, their utilization is somewhat constrained by their intrinsic low-resolution capability, particularly in complex applications such as coastal zone monitoring and analysis of typhoons. To overcome this limitation, resolution enhancements are necessary. Here, we present a
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3-D Line Segment Reconstruction With Depth Maps for Photogrammetric Mesh Refinement in Man-Made Environments IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Tong Fang, Min Chen, Han Hu, Wen Li, Xuming Ge, Qing Zhu, Bo Xu
3-D line segments contain richer geometric and structural information than 3-D point clouds in man-made environments, which is beneficial for providing constraints to refine point-cloud-based mesh models or build accurate wireframes. However, the efficient reconstruction of 3-D line segments with high scene coverage from multiview images is still challenging. In this study, the depth maps obtained
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Boundary-Semantic Collaborative Guidance Network With Dual-Stream Feedback Mechanism for Salient Object Detection in Optical Remote Sensing Imagery IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Dejun Feng, Hongyu Chen, Suning Liu, Ziyang Liao, Xingyu Shen, Yakun Xie, Jun Zhu
With the increasing application of deep learning (DL) in various domains, salient object detection in optical remote sensing images (ORSIs-SOD) has attracted significant attention. However, most existing ORSI-SOD methods predominantly rely on local information from low-level features to infer salient boundary cues and supervise them using boundary ground truth (GT) but fail to sufficiently optimize
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An Advanced Echo Separation Scheme Based on Multinull Constraint Beamformer With Deepened Nulls IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Rongxiang Wang, Kai Qiao, Yunkai Deng, Wei Wang, Yue Liu, Zhen Chen, Jinsong Qiu, Sheng Chang
The multiple elevation beam (MEB) mode is an effective technique for enhancing imaging width in spaceborne synthetic aperture radar (SAR) systems. This mode combines intrapulse beam-steering during transmitting and digital beamforming (DBF) during receiving. By sequentially illuminating the far sub-swath followed by the near sub-swath, echoes from different sub-swaths can reach the antenna at the same
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Super-Resolution Mapping With a Fraction Error Eliminating CNN Model IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Zhixiang Yin, Yanlan Wu, Penghai Wu, Zhen Hao, Feng Ling
Super-resolution mapping (SRM) is an effective way to alleviate the mixed pixel problem of remotely sensed imagery by transforming the coarse-resolution fraction image originating from spectral unmixing into a fine-resolution land cover map. Deep learning has been widely used in SRM since it has a powerful ability to represent the complex heterogeneous spatial distribution patterns of land cover patches;
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Modality Registration and Object Search Framework for UAV-Based Unregistered RGB-T Image Salient Object Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Kechen Song, Hongwei Wen, Xiaotong Xue, Liming Huang, Yingying Ji, Yunhui Yan
Unmanned aerial vehicles (UAVs) are widely used in various industries, and various visual tasks under the perspective of the UAV have been widely studied. In particular, the red-green-blue-T (RGB-T) detection method based on UAVs has shown significant advantages. However, the existing RGB-T methods are designed based on registration image pairs rather than detecting images directly acquired by UAVs
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QIS-GAN: A Lightweight Adversarial Network With Quadtree Implicit Sampling for Multispectral and Hyperspectral Image Fusion IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Chunyu Zhu, Shangqi Deng, Yingjie Zhou, Liang-Jian Deng, Qiong Wu
Multispectral and hyperspectral image fusion (MHIF) involves the fusion of high-spatial-resolution multispectral images (HR-MSIs) and low-spatial-resolution hyperspectral images (LR-HSIs) to generate high-spatial-resolution hyperspectral images (HR-HSIs) and has gained significant attention in the field of remote-sensing imaging. While CNN and Transformer models have shown effectiveness in MHIF, existing
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Knowledge-Aided Momentum Contrastive Learning for Remote-Sensing Image Text Retrieval IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-13 Zhong Ji, Changxu Meng, Yan Zhang, Yanwei Pang, Xuelong Li
Remote-sensing image–text retrieval (RSITR) has attracted widespread attention due to its great potential for rapid information mining ability on remote-sensing images. Although significant progress has been achieved, existing methods typically overlook the challenge posed by the extremely analogous descriptions, where the subtle differences remain largely unexploited or, in some cases, are entirely
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Hyperspectral Image Classification via Cross-Domain Few-Shot Learning With Kernel Triplet Loss IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-10 Ke-Kun Huang, Hao-Tian Yuan, Chuan-Xian Ren, Yue-En Hou, Jie-Li Duan, Zhou Yang
Limited labeled training samples constitute a challenge in hyperspectral image classification, with much research devoted to cross-domain adaptation, where the classes of the source and target domains are different. Current cross-domain few-shot learning (FSL) methods only use a small number of sample pairs to learn the discriminant features, which limits their performance. To address this problem
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On the Scattering-Angle Dependence of the Spectral Consistency of Ice Cloud Optical Thickness Retrievals Based on Geostationary Satellite Observations IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Dongchen Li, Masanori Saito, Ping Yang, Norman G. Loeb, William L. Smith, Patrick Minnis
Visible-near infrared (VIS-NIR) and thermal infrared (TIR) methods have long been used for ice cloud property retrievals based on satellite observations. Both retrieval methods are sensitive to the assumed ice particle models, which can significantly impact the accuracy of the retrieved microphysical and radiative properties of ice clouds. The two-habit model (THM) is considered a suitable ice particle
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NLSAN: A Non-Local Scene Awareness Network for Compact Polarimetric ISAR Image Super-Resolution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Ming-Dian Li, Jun-Wu Deng, Shun-Ping Xiao, Si-Wei Chen
Polarimetric inverse synthetic aperture radar (ISAR) can operate all-day and all-weather, making it crucial for space surveillance. The compact polarimetric (CP) mode balances hardware complexity and polarimetric information, which is commonly equipped with ISAR systems. Given the constraints of limited physical conditions, exploring ISAR image super-resolution is worthwhile. Currently, deep learning
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Self-Supervised Learning With Learnable Sparse Contrastive Sampling for Hyperspectral Image Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Miaomiao Liang, Jian Dong, Lingjuan Yu, Xiangchun Yu, Zhe Meng, Licheng Jiao
Contrastive learning (CL) with learnable examples performs outstandingly in data representation. However, when dealing with hard samples, instance-level alignment with excessive uniformity may descend into trivial clusters, especially when confronted with interclass similarity and intraclass diversity in hyperspectral images (HSIs). To solve this problem, we regard prototypical CL as tracing the potential
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PointGame: Geometrically and Adaptively Masked Autoencoder on Point Clouds IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Yun Liu, Xuefeng Yan, Zhiqi Li, Zhilei Chen, Zeyong Wei, Mingqiang Wei
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity. We propose a geometrically and adaptively masked autoencoder on point clouds for self-supervised learning, termed PointGame. PointGame contains two core components: GATE and EAT. GATE stands for
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Hierarchical Feature Fusion of Transformer With Patch Dilating for Remote Sensing Scene Classification IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Xiaoning Chen, Mingyang Ma, Yong Li, Shaohui Mei, Zonghao Han, Jian Zhao, Wei Cheng
Recently, the Transformer-based technique has emerged as a promising solution for modeling contextual information in remote sensing (RS) scenes and has found widespread applications in RS scene classification. However, how to make full use of intermediate features learned in Transformers is of crucial importance in the RS scene classification tasks. Therefore, this article proposes a hierarchical feature
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GeoFormer: A Geometric Representation Transformer for Change Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Jiaxuan Zhao, Licheng Jiao, Chao Wang, Xu Liu, Fang Liu, Lingling Li, Shuyuan Yang
Deep representation learning has improved automatic remote sensing change detection (RSCD) in recent years. Existing methods emphasize primarily convolutional neural networks (CNNs) or transformer-based networks. However, most of them neither effectively combine CNNs and transformers nor use prior geometric information to refine regions. In this article, a novel geometric representation transformer
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Parallelized 3-D Inversion of Controlled-Source Electromagnetic Data Based on Spectral Element Method With Infinite Element Boundary IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Jintong Xu, Xiao Xiao, Jingtian Tang, Cheng Pang, Diquan Li
This study developed an efficient 3-D inversion algorithm for the controlled-source electromagnetic method (CSEM). The spectral element method based on high-order Gauss-Lobatto-Legendre (GLL) basis functions with infinite element boundary conditions is used to quickly solve forward problems and adjoint forward problems for inversion, which can significantly reduce the computational cost while guarantee
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VoxelNextFusion: A Simple, Unified, and Effective Voxel Fusion Framework for Multimodal 3-D Object Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Ziying Song, Guoxin Zhang, Jun Xie, Lin Liu, Caiyan Jia, Shaoqing Xu, Zhepeng Wang
Light detection and ranging (LiDAR)–camera fusion can enhance the performance of 3-D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including
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Diffraction Separation and Least-Squares Imaging Based on Multiscale and Multidirectional Wavefield and Image Decomposition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-09 Chuang Li, Shixuan Jia, Zhen Li, Zhaoqi Gao, Feipeng Li, Liang Zhao, Jinghuai Gao
Diffraction separation and imaging are important for subsurface discontinuities characterization. However, conventional diffraction separation methods may lose validity when the diffractions and reflections do not have discernible differences in data domain. Moreover, due to limited acquisition geometry and narrow frequency band of seismic data, the diffraction imaging methods that use conventional
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Retrieval of Atmospheric Water Vapor Profiles From COSMIC-2 Radio Occultation Constellation Using Machine Learning IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Soumil Hooda, Manik Gupta, Randhir Singh, Satya P. Ojha
This study uses data from January 2020 to July 2022 to develop an artificial neural network (ANN)-based model to retrieve atmospheric water vapor (Wv) pressure profiles from Constellation Observing System for Meteorology, Ionosphere (COSMIC-2) observations over the Indian region. Prior to using the COSMIC-2 (C2) refractivity, we evaluated the C2 refractivity’s quality by comparing it with radiosonde
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Spatial and Spectral Structure Preserved Self-Representation for Unsupervised Hyperspectral Band Selection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Chang Tang, Jun Wang, Xiao Zheng, Xinwang Liu, Weiying Xie, Xianju Li, Xinzhong Zhu
As an effective manner to reduce data redundancy and processing inconvenience, hyperspectral band selection aims to select a subset of informative and discriminative bands from the original data cube. Although a large number of approaches have been proposed and obtained great success, they still face at least two issues. First, most of the previous methods only consider the redundancy between neighbor
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Multicue Contrastive Self-Supervised Learning for Change Detection in Remote Sensing IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Meijuan Yang, Licheng Jiao, Fang Liu, Biao Hou, Shuyuan Yang, Yake Zhang, Jianlong Wang
Contrastive self-supervised learning (CSSL) is a promising method for extracting effective features from unlabeled data. It performs well in image-level tasks, such as image classification and retrieval. However, the existing CSSL methods are not suitable for pixel-level tasks, for example, change detection (CD), since they ignore the correlation between local patches or pixels. In this article, we
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LaMIE: Large-Dimensional Multipass InSAR Phase Estimation for Distributed Scatterers IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Yusong Bai, Jian Kang, Xiang Ding, Anping Zhang, Zhe Zhang, Naoto Yokoya
State-of-the-art (SOTA) phase linking (PL) methods for distributed scatterer (DS) interferometry (DSI) retrieve consistent phase histories from the sample coherence matrix or the one whose magnitudes are calibrated. To unify them, we first propose a framework consisting of sample coherence matrix estimation and Kullback–Leibler (KL) divergence minimization. Within such framework, we observe that the
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Vertical Synchrosqueezing for High-Resolution Radar Imaging IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Karol Abratkiewicz, Jȩdrzej Drozdowicz, Piotr Samczyński
This article presents a novel approach to inverse synthetic aperture radar (ISAR) image enhancement using time-frequency (TF) vertical synchrosqueezing (VSS). The authors proposed adding the signal extraction block to the ISAR image processing path. Since the VSS technique does not change the signal amplitude-phase dependencies but only performs its decomposition and denoising, the proposed method
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Learning Transferable Discriminative Knowledge From Attribute-Aligned Hyperspectral Images IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Qichao Liu, Liang Xiao, Nan Huang, Jinhui Tang
Hyperspectral image (HSI) classification faces the inherent challenge of small sample learning, primarily due to the difficulty in labeling vast land covers. Meta-learning, with its ability to learn transferable meta-knowledge from existing HSIs, is seen as a promising solution. However, different HSIs usually have varying distributions manifested as differing spectral wavelengths and reflectance shifts
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AMSSE-Net: Adaptive Multiscale Spatial–Spectral Enhancement Network for Classification of Hyperspectral and LiDAR Data IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Hongmin Gao, Hao Feng, Yiyan Zhang, Shufang Xu, Bing Zhang
With the abundant emergence of remote sensing (RS) data sources, multimodal remote sensing observation has become an active field. Extracting valuable information from multimodal data has the potential to make a significant contribution to applications such as urban planning and monitoring. However, existing studies are deficient in extracting spectral and spatial features from hyperspectral (HS) remote
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3-D Joint Inversion of Gravity and Magnetic Data Based on a Deep Learning Network With Automatic Recognition of Structural Similarity IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Zhiwen Zhou, Jun Wang, Xiaohong Meng, Yuan Fang
The data-driven inversion methods based on deep learning (DL) for gravity and magnetic data have been well studied in existing literature due to their advantages such as less constraint requirements and extremely high efficiency compared with various conventional model-driven inversion methods. However, some issues should be further addressed when these methods are applied to the joint inversion, such
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Advancing CO2 Storage Monitoring via Cross-Borehole Apparent Resistivity Imaging Simulation IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Nian Yu, Hanghang Liu, Xiao Feng, Tianyang Li, Bingrui Du, Chenguang Wang, Wuji Wang, Wenxin Kong
Conventional resistivity inversion methodologies encounter constraints in perpetual monitoring owing to the necessity for recurrent measurements. In response, this research leverages a 3-D finite element method to formulate an approximate geometry imaging of cross-borehole resistivity during forward modeling, circumventing the direct computation of Jacobian matrix equations in the electric field. This
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Investigating Terrestrial Water Storage Changes and Their Driving Factors in the Southwest River Basin of China Using Geodetic Data IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-08 Xianpao Li, Bo Zhong, Jiancheng Li, Haihong Wang
The space geodetic technologies [e.g., global navigation satellite system (GNSS) and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO)] provide effective tools to infer terrestrial water storage (TWS) change, which is an important indicator of the hydrological cycle and climate change. This study investigated the optimization approaches of the Slepian basis function (SBF) method
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Effect of Spherical Wavefronts on Very-High-Frequency (VHF) Lightning Interferometer Observations IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Xiangpeng Fan, Paul R. Krehbiel, Mark A. Stanley, Yijun Zhang, William Rison, Harald E. Edens
Interferometric measurements of very-high-frequency (VHF) radio frequency signals produced by lightning are one of the most effective techniques for studying lightning breakdown processes, so uncertainty and error analyses of interferometric location results have become important topics. Based on the plane wave approximation of lightning RF signal transmission for interferometric location, a geometric
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Persymmetric Adaptive Radar Target Detection in CG-LN Sea Clutter Using Complex Parameter Suboptimum Tests IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Jian Xue, Zhen Fan, Shuwen Xu, Jun Liu
In this article, we consider the detection problem of marine radar targets embedded in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with lognormal texture (CG-LN) and unknown covariance matrices. To reduce the dependence of detectors on training data, the original radar data are transformed via exploiting the persymmetric structure of clutter covariance matrix
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SAR–Optical Image Matching With Semantic Position Probability Distribution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Liangzhi Li, Ling Han, Ming Liu, Kyle Gao, Hongjie He, Lanying Wang, Jonathan Li
We propose a deep learning framework of semantic position probability distribution for synthetic aperture radar (SAR)-optical image matching, termed as SPPD. Unlike the pixel-by-pixel searching matching method, a correspondence is directly obtained by an outputted matching position probability distribution. First, multiscale pyramidal features are created for each pixel in the SAR and optical images
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Self-Supervised Classification of SAR Images With Optical Image Assistance IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Chenxuan Li, Weiwei Guo, Zenghui Zhang, Tao Zhang
Supervised deep neural networks (DNNs) have proven to be powerful tools for synthetic aperture radar (SAR) image interpretation tasks. However, they present a formidable challenge in acquiring a substantial amount of labeled data. In this article, we investigate the promising technique of contrastive self-supervised learning for SAR image classification. This approach allows us to take advantage of
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Attention Mechanism With Spatial Spectrum Dense Connection and Context Dynamic Convolution for Cloud Detection IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Jing Zhang, Liangnong Song, Yuchen Wang, Jun Wu, Yunsong Li
Rapid advances in remote sensing technology have allowed its extensive use in defense, land use planning, urban traffic monitoring, and natural disaster warning. Remote sensing technology has penetrated every aspect of modern life. However, some problems need to be solved in the use of remote sensing data, such as the presence of clouds in images. Efficient air–ground data transmission can be realized
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Missing Sonic Logs Generation for Gas Hydrate-Bearing Sediments via Hybrid Networks Combining Deep Learning With Rock Physics Modeling IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Zikun Li, Jialong Xia, Zhichao Liu, Gang Lei, Kyungbook Lee, Fulong Ning
Logging-while-drilling (LWD) sonic data are critical for marine gas hydrate reservoir evaluation and production prediction. However, acquiring complete acoustic logs, particularly shear wave, poses significant challenges and incurs high costs. To tackle this issue, we develop a two-branch hybrid framework for predicting LWD sonic logs of hydrate-bearing sediments from existing logging data. One branch
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Surface Gravity Response of CO2 Storage in the Johansen Deep Reservoir IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Maurizio Milano, Maurizio Fedi
This study regards the assessment of surface gravity surveying for CO2 plume monitoring in the deep Johansen saline aquifer, a potential offshore site for CO2 geologic storage. We used the available benchmark model and geological information to simulate the injection and postinjection phases. We calculated the gravity response at the surface from the estimated models of reservoir density and saturation
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Prediction of Ship Motion Attitude From Radial Velocity of Water Particle Using Coherent S-Band Radar IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Yunyu Wei, Zezong Chen, Chen Zhao, Xi Chen, Chunyang Zhang, Jiangheng He
Accurate prediction of the ship motion attitude in the future period is important to ensure the safety of offshore operations and sea navigation. Coherent S-band radar is a novel wave monitoring device that can be applied to advance the development of ship motion attitude prediction. In this study, we propose a method for realizing ship motion attitude prediction from radial velocity of water particle
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RAN: Region-Aware Network for Remote Sensing Image Super-Resolution IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-07 Baodi Liu, Lifei Zhao, Shuai Shao, Weifeng Liu, Dapeng Tao, Weijia Cao, Yicong Zhou
The remote sensing (RS) image super-resolution (SR) algorithm aims to reconstruct a high-resolution (HR) image with rich texture details from a given low-resolution (LR) image, improving the spatial resolution. It has been widely concerned in RS image processing and application. Most current deep-learning-based methods rely on paired training datasets. However, most datasets are often based on bicubic
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Electromagnetic Scattering for Multiple Moving Targets Above/On a Rough Surface Using Multi-Dynamic-Octrees-Based SBR Algorithm IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Wei Meng, Juan Li, Yong-Ji Xi, Li-Xin Guo, Shun-Kang Wen
For fast solving the electromagnetic scattering of multiple moving targets above or on a rough surface, the multi-dynamic-octrees-based shooting and bouncing ray (SBR) algorithm is proposed in the article. First, a local coordinate system (LCS) for each object is established that moves with targets in the geodetic coordinate system (GCS). The dynamic octree structure based on LCS is applied to keep
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Evaluating the Radiometric Performance of the Clouds and the Earth’s Radiant Energy System (CERES) Instruments on Terra and Aqua Over 20 Years IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Mohan Shankar, Norman G. Loeb, Nathaniel Smith, Natividad Smith, Janet L. Daniels, Susan Thomas, Dale Walikainen
Six Clouds and the Earth’s Radiant Energy System (CERES) instruments on four satellites are used to produce a global continuous multidecadal record of Earth’s radiation budget (ERB) at the top-of-atmosphere (TOA). Each CERES instrument was calibrated and characterized on the ground before launch, while postlaunch calibration was conducted using onboard calibration sources. The performance of the CERES
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Applicability of the γ–R Relationship in Rainfall Measurement With Microwave Link Under Tilted Conditions: A Simulation Analysis IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Kang Pu, Xichuan Liu, Lei Liu, Xuejin Sun, Jin Ye, Peng Zhang
Microwave links as a novel method of rainfall monitoring have been extensively studied over the last two decades. Still, the applicability of $\gamma $ (rain-induced attenuation rate)– $R$ (rain rate) relationship in quantitative rainfall inversion with microwave link under tilted conditions has not been systematically investigated to date. With the establishment of rain-induced attenuation fields
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Developing a Radar Signal Simulator for the Community Radiative Transfer Model IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Isaac Moradi, Benjamin Johnson, Patrick Stegmann, Daniel Holdaway, Gerald Heymsfield, Ronald Gelaro, Will McCarty
Active radar instruments provide vertically resolved clouds and precipitation measurements that cannot be provided by the passive instruments. These active measurements are not conventionally assimilated into the data assimilation systems because of the lack of fast forward radiative transfer (RT) models and also difficulties in the error modeling of the measurements. This article describes the development
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Causal Adversarial Autoencoder for Disentangled SAR Image Representation and Few-Shot Target Recognition IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Qian Guo, Huilin Xu, Feng Xu
Lack of interpretability and weak generalization ability have become the major challenges with data-driven intelligent synthetic aperture radar-automatic target recognition (SAR-ATR) technology, especially in practical applications with azimuth-sparse training samples. A novel insight into SAR image representation with neural networks from a causal perspective is presented in this article. First, a
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A Convolutional De-Quantization Network for Harmonics Suppression in One-Bit SAR Imaging IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Cuiqi Si, Bo Zhao, Lei Huang, Shiqi Liu
One-bit synthetic aperture radar (SAR) imaging, which collects echoes into one-bit quantized samples, is a highly promising technique for the faster SAR images acquisition and simplified implementations in numerous applications. Harmonics, accompanied with one-bit sampling operation, are one of the fundamental factors that trigger severe aliasing artifacts problem in frequency domain. For one-bit SAR
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Hypothesis Margin-Based Ensemble Method for the Classification of Noisy Remote Sensing Data IEEE Trans. Geosci. Remote Sens. (IF 8.2) Pub Date : 2023-11-06 Wei Feng, Xinting Gao, Samia Boukir, Zhiwei Xie, Yinghui Quan, Wenjiang Huang, Mengdao Xing
The accuracy of a classifier, whether it is an ensemble or not, is directly influenced by the training data used in learning. In remote sensing, training data mislabeling is inevitable and faces a major challenge. This article proposes a versatile data cleaning, which handles the mislabeling problem by exploiting the ensemble concepts for identifying and then eliminating or correcting the mislabeled