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IEEE Transactions on Geoscience and Remote Sensing最新文献

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PSRNet: A Progressive Self-refine Network for Lightweight Optical Remote Sensing Image Dehazing PSRNet：用于轻量级光学遥感图像去重的渐进式自精细网络
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3489964
Shuoshi Li, Yuan Zhou, Sun-Yuan Kung
{"title":"PSRNet: A Progressive Self-refine Network for Lightweight Optical Remote Sensing Image Dehazing","authors":"Shuoshi Li, Yuan Zhou, Sun-Yuan Kung","doi":"10.1109/tgrs.2024.3489964","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3489964","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580441","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
Multi-level Denoising for High Quality SAR Object Detection in Complex Scenes 在复杂场景中进行多级去噪实现高质量合成孔径雷达目标检测
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3489212
Wei Liu, Lifan Zhou
{"title":"Multi-level Denoising for High Quality SAR Object Detection in Complex Scenes","authors":"Wei Liu, Lifan Zhou","doi":"10.1109/tgrs.2024.3489212","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3489212","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580435","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
Structure-Guided Multiscale Impedance Inversion Based on Modified Total Variation Regularization 基于修正总变异正则化的结构引导型多尺度阻抗反演
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3491212
Hao Li, Yian Cui, Pu Wang, Youjun Guo, Yang Yuan, Pengfei Zhang, Jianxin Liu
{"title":"Structure-Guided Multiscale Impedance Inversion Based on Modified Total Variation Regularization","authors":"Hao Li, Yian Cui, Pu Wang, Youjun Guo, Yang Yuan, Pengfei Zhang, Jianxin Liu","doi":"10.1109/tgrs.2024.3491212","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3491212","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580434","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
Quantifying the compatibility of optical reflectance factors in a field intercomparison experiment 在实地相互比较实验中量化光学反射系数的兼容性
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3488785
Javier Pacheco-Labrador, Juanjo Peón, Marcos Jiménez, José Ramón Rodríguez-Pérez, José A. J. Berni, David Aragonés, Ricardo Díaz-Delgado, José Dorado, Ana De Castro, M. Pilar Martín
{"title":"Quantifying the compatibility of optical reflectance factors in a field intercomparison experiment","authors":"Javier Pacheco-Labrador, Juanjo Peón, Marcos Jiménez, José Ramón Rodríguez-Pérez, José A. J. Berni, David Aragonés, Ricardo Díaz-Delgado, José Dorado, Ana De Castro, M. Pilar Martín","doi":"10.1109/tgrs.2024.3488785","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3488785","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580436","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 Novel Downscaling Approach based on Multi-Frequency Microwave Radiometry toward Finer Scale Global Soil Moisture Mapping 基于多频微波辐射测量的新型降尺度方法，实现更精细的全球土壤水分绘图
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3490758
Peilin Song, Tianjie Zhao, Jiancheng Shi, Yongqiang Zhang, Jingyao Zheng
{"title":"A Novel Downscaling Approach based on Multi-Frequency Microwave Radiometry toward Finer Scale Global Soil Moisture Mapping","authors":"Peilin Song, Tianjie Zhao, Jiancheng Shi, Yongqiang Zhang, Jingyao Zheng","doi":"10.1109/tgrs.2024.3490758","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3490758","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580429","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
Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection 用于高光谱变化检测的量子信息赋能图神经网络
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3490703
Chia-Hsiang Lin, Tzu-Hsuan Lin, Jocelyn Chanussot
{"title":"Quantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection","authors":"Chia-Hsiang Lin, Tzu-Hsuan Lin, Jocelyn Chanussot","doi":"10.1109/tgrs.2024.3490703","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3490703","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580432","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
Negative Samples Mining Matters: Reconsidering Hyperspectral Image Classification with Contrastive Learning 负样本挖掘问题：利用对比学习重新考虑高光谱图像分类
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3491074
Hui Liu, Chenjia Huang, Ning Chen, Tao Xie, Mingyue Lu, Zhou Huang
{"title":"Negative Samples Mining Matters: Reconsidering Hyperspectral Image Classification with Contrastive Learning","authors":"Hui Liu, Chenjia Huang, Ning Chen, Tao Xie, Mingyue Lu, Zhou Huang","doi":"10.1109/tgrs.2024.3491074","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3491074","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580443","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
High-Resolution Remote Sensing Image Segmentation With Global-Guided Normalization and Local Affinity Distillation 利用全局引导归一化和局部亲和性蒸馏技术进行高分辨率遥感图像分割
IF 7.5 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/TGRS.2024.3482688
Peng Zhu;Xiangrong Zhang;Xiao Han;Puhua Chen;Xu Tang;Xina Cheng;Licheng Jiao
{"title":"High-Resolution Remote Sensing Image Segmentation With Global-Guided Normalization and Local Affinity Distillation","authors":"Peng Zhu;Xiangrong Zhang;Xiao Han;Puhua Chen;Xu Tang;Xina Cheng;Licheng Jiao","doi":"10.1109/TGRS.2024.3482688","DOIUrl":"10.1109/TGRS.2024.3482688","url":null,"abstract":"In recent years, high-resolution (HR) remote sensing images (RSIs) segmentation has received growing attention. The huge number of pixels poses a challenge to the semantic segmentation algorithm, which is limited by the storage of GPUs, so the current methods for processing HR RSIs are categorized into two main categories, i.e., global methods and local methods. The former downsamples the original image and loses a lot of feature details. The latter crops the original image and fails to obtain global contextual information. Both types of methods lead to limited segmentation accuracy. In this article, we propose an end-to-end framework, called global injection network (GINet), which explores two levels of feature distribution and feature relationship to achieve tradeoff between global context and local details. In concrete terms, we propose the global-guided normalization (GGN) module, which injects global context information into local branch and modulates local features using global features to enhance the global perception of local branch. In addition, to constrain the spatial consistency of two branches, inspired by the knowledge distillation technique, we propose local affinity distillation (LAD) loss, which distills the relations in local features into global features to keep the similarity of the relationships corresponding to patches in the two branches. The comprehensive experimental results on three large-scale land-cover classification datasets, DeepGlobe (\u0000<inline-formula> <tex-math>$2448 times 2448$ </tex-math></inline-formula>\u0000), Inria Aerial (\u0000<inline-formula> <tex-math>$5000 times 5000$ </tex-math></inline-formula>\u0000), and GID-15 (\u0000<inline-formula> <tex-math>$7200 times 6800$ </tex-math></inline-formula>\u0000), confirm the effectiveness and superiority of our method in HR semantic segmentation tasks.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580439","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
MCFT: Multimodal Contrastive Fusion Transformer for Classification of Hyperspectral Image and LiDAR Data MCFT：用于高光谱图像和激光雷达数据分类的多模态对比融合变换器
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3490752
Yining Feng, Jiarui Jin, Yin Yin, Chuanming Song, Xianghai Wang
{"title":"MCFT: Multimodal Contrastive Fusion Transformer for Classification of Hyperspectral Image and LiDAR Data","authors":"Yining Feng, Jiarui Jin, Yin Yin, Chuanming Song, Xianghai Wang","doi":"10.1109/tgrs.2024.3490752","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3490752","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580431","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
Adaptive Multiscale Slimming Network Learning for Remote Sensing Image Feature Extraction 用于遥感图像特征提取的自适应多尺度瘦身网络学习
IF 8.2 1区 地球科学
IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-04 DOI: 10.1109/tgrs.2024.3490666
Dingqi Ye, Jian Peng, Wang Guo, Haifeng Li
{"title":"Adaptive Multiscale Slimming Network Learning for Remote Sensing Image Feature Extraction","authors":"Dingqi Ye, Jian Peng, Wang Guo, Haifeng Li","doi":"10.1109/tgrs.2024.3490666","DOIUrl":"https://doi.org/10.1109/tgrs.2024.3490666","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580442","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
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