IEEE Transactions on Geoscience and Remote Sensing最新文献_第10页

Influence of Radiative Transfer Model-based Atmospheric Correction and Dynamic Tie Points on Sea Ice Concentration Retrieval from near-90 GHz algorithm with FY-3D MWRI data

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3551941

Yufang Ye, Ziyu Yan, Xin Wang, Zhouqi Chen, Mohammed Shokr, Xiao Cheng

引用次数: 0

A Two-stage Hierarchical Spatiotemporal Fusion Network for Land Surface Temperature with Transformer

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3552577

Penghua Hu, Xin Pan, Yingbao Yang, Yang Dai, Yuncheng Chen

引用次数: 0

Recursive 3D Phase Unwrapping for Reliable Deformation Anomaly Detection in Multi-temporal SAR Interferometry

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3552491

Fengming Hu, Siyu Cheng, Yikai Liu, Feng Xu

引用次数: 0

Geospatial Time Machine: A Generative Model to Enhance Spectral-Temporal Data Resolution

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3552629

Felipe A. Lopes, Vasit Sagan, Supria Sarkar, Abby Stylianou, Flavio Esposito

引用次数: 0

CromSS: Cross-modal pretraining with noisy labels for remote sensing image segmentation

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3552642

Chenying Liu, Conrad M Albrecht, Yi Wang, Xiao Xiang Zhu

引用次数: 0

RMSO-ConvNeXt: A Lightweight CNN Network for Robust SAR and Optical Image Matching under Strong Noise Interference

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-18 DOI: 10.1109/tgrs.2025.3550936

Chao Yang, Guoqing Gong, Chang Liu, Jiwei Deng, Yuanxin Ye

引用次数: 0

On the Deceptive Jamming Technique Against Video Synthetic Aperture Radar

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-17 DOI: 10.1109/tgrs.2025.3551797

Ying Zhang, Da Zhi Ding, Zi He, Henry Leung

引用次数: 0

A Multivehicle Tracking Method for Video-SAR With Reliable Foreground–Background Motion Feature Compensation

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-17 DOI: 10.1109/TGRS.2025.3551692

Jianzhi Hong;Taoyang Wang;Yuqi Han;Weicheng Di;Tiancheng Dong

{"title":"A Multivehicle Tracking Method for Video-SAR With Reliable Foreground–Background Motion Feature Compensation","authors":"Jianzhi Hong;Taoyang Wang;Yuqi Han;Weicheng Di;Tiancheng Dong","doi":"10.1109/TGRS.2025.3551692","DOIUrl":"10.1109/TGRS.2025.3551692","url":null,"abstract":"Most of the existing video synthetic aperture radar (ViSAR) vehicle multitarget tracking (MTT) methods only perform interframe association based on the idea of appearance modeling, and are not closely integrated with the ViSAR moving target imaging characteristics, resulting in limited accuracy improvement of existing MTT methods. ViSAR moving targets have the characteristics of individual similarity, time-varying appearance, and background pseudo-motion, which have a great impact on tracking performance. In this regard, we propose a multivehicle tracking method for ViSAR with reliable foreground-background motion feature compensation (RFBMFC). Specifically, in order to improve the distinguishability of individual features, the spatial-temporal semantic sparse alignment (STSSA) module with intraframe and interframe context key information aggregation and interaction is constructed in the feature extraction stage, which can generate more accurate dense optical flow to enhance the detection and association of foreground targets. In order to improve the tracking continuity of foreground targets with time-varying appearance, the shadow-observation-state mining (SOSM) module is designed in the interframe association stage, which can cluster targets under different appearance states and adaptively restore lost target trajectories. In addition, the background motion fast compensation (BMFC) module is designed, which can learn background motion estimation and correct the trajectory prediction error of foreground targets in an end-to-end self-supervised manner to improve the MTT accuracy under camera motion. Tests on datasets captured by Sandia National Laboratories (SNL) and Beijing Institute of Radio Measurement (BIRM) show that RFBMFC outperforms many representative MTT methods. Compared with the suboptimal method, RFBMFC improves the multiobject tracking accuracy (MOTA) by 1.10% on the SNL data, and by 5.00% on the BIRM data, verifying the effectiveness of RFBMFC.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-22"},"PeriodicalIF":7.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Prestack Elastic Parameter Seismic Inversion Method Based on xLSTM-Unet

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-17 DOI: 10.1109/TGRS.2025.3551769

Chuhan Zheng;Xingye Liu;Pengqi Wang;Qingchun Li;Feifan He

{"title":"A Prestack Elastic Parameter Seismic Inversion Method Based on xLSTM-Unet","authors":"Chuhan Zheng;Xingye Liu;Pengqi Wang;Qingchun Li;Feifan He","doi":"10.1109/TGRS.2025.3551769","DOIUrl":"10.1109/TGRS.2025.3551769","url":null,"abstract":"Prestack seismic data retain the amplitude variation with offset (AVO) characteristics, providing more geophysical information reflecting lateral reservoir variations, thus facilitating the identification of oil and gas reservoirs. However, due to the band-limited nature of seismic data, the precision of forward modeling, and the accuracy of algorithms, traditional prestack approaches suffer from ambiguity and uncertainty. With the development of deep learning and big data, an increasing number of deep learning methods have been proposed. We integrate the extended long short-term memory (xLSTM) modules with the Unet framework, and design a novel neural network architecture, that is, xLSTM-Unet, for elastic parameter inversion (<inline-formula> <tex-math>$V_{text {P}}$ </tex-math></inline-formula>, <inline-formula> <tex-math>$V_{text {S}} $ </tex-math></inline-formula>, and <inline-formula> <tex-math>$rho $ </tex-math></inline-formula>) from prestack seismic gathers. Through testing on synthetic seismic records and field data, the proposed xLSTM-Unet outperforms both the traditional Unet and LSTM-Unet models in predicting elastic parameters from prestack seismic data. The xLSTM-Unet proposed in this article provides a stable and effective approach for predicting prestack elastic parameters, offering new insights for the intelligent development of seismic exploration.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-13"},"PeriodicalIF":7.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperspectral Object Tracking With Context-Aware Learning and Category Consistency

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-03-17 DOI: 10.1109/TGRS.2025.3551724

Ye Wang;Shaohui Mei;Mingyang Ma;Yuheng Liu;Tao Gao;Huiyang Han

{"title":"Hyperspectral Object Tracking With Context-Aware Learning and Category Consistency","authors":"Ye Wang;Shaohui Mei;Mingyang Ma;Yuheng Liu;Tao Gao;Huiyang Han","doi":"10.1109/TGRS.2025.3551724","DOIUrl":"10.1109/TGRS.2025.3551724","url":null,"abstract":"Hyperspectral imaging technology is of crucial importance to improve the performance of object tracking in many remote sensing surveillance areas. Previous methods primarily focused on feature fusion strategies by employing additional enhancement modules. However, these methods commonly lack contextual understanding to distinguish the target from the background and totally ignore the category information of the targets. To address these limitations, a novel hyperspectral object tracker is proposed to incorporate context-aware learning and category consistency tracker (CCTrack), which can adaptively learn context-aware representations in hyperspectral scenarios to obtain global target information with memory storage, while constructing an interframe category consistency constraint to enhance tracking process. Specifically, CCTrack integrates an adaptive context-aware learning (ACL) mechanism, which includes a feature decoupling module (FDM) to extract specific representations from decoupled features, and a Mamba layer to retain and update long-range dependencies. To align with prior knowledge of target recognition and motion patterns, an alignment transformation module (ATM) is employed with the ACL mechanism, fully leveraging spatial-spectral representations. In addition, category consistency constraint modules (C3Ms) are introduced to enforce category consistency across frames by computing the similarities between the target features and the corresponding category name, serving as the constraint to improve tracking performance. Extensive experiments over the hyperspectral object tracking (HOT) benchmark covering various remote sensing scenarios demonstrate that CCTrack outperforms state-of-the-art methods by a significant margin.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-16"},"PeriodicalIF":7.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0