Pub Date : 2026-02-13DOI: 10.1109/tgrs.2026.3664714
Dajiang Lei, Kai Zhang, Liping Zhang, Yongtao Deng, Yidong Peng, Weisheng Li
{"title":"Nonlocal Deep Unfolding Pansharpening Method Based on Dynamic Kernel Prior","authors":"Dajiang Lei, Kai Zhang, Liping Zhang, Yongtao Deng, Yidong Peng, Weisheng Li","doi":"10.1109/tgrs.2026.3664714","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3664714","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"32 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146198446","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}
Pub Date : 2026-02-13DOI: 10.1109/tgrs.2026.3664446
Sinan Çavdar, Selim Aksoy
{"title":"CODI: Contextual Object Detection via Image Inpainting","authors":"Sinan Çavdar, Selim Aksoy","doi":"10.1109/tgrs.2026.3664446","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3664446","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"110 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146198447","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}
Pub Date : 2026-02-12DOI: 10.1109/tgrs.2026.3664181
Fan Yang, Cunren Liang, Yuhang Wang
{"title":"InSAR Phase Errors due to Range Misregistrations","authors":"Fan Yang, Cunren Liang, Yuhang Wang","doi":"10.1109/tgrs.2026.3664181","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3664181","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"36 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169622","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}
Pub Date : 2026-02-12DOI: 10.1109/tgrs.2026.3664291
Lu Chen, Hao Zhu, Pengyu Tian, Xiaotong Li, Ye Sun, Jinguo Hu, Biao Hou
{"title":"MultiPC: Multi-Task Learning for Joint Pansharpening and Classification from Multispectral and Panchromatic Images","authors":"Lu Chen, Hao Zhu, Pengyu Tian, Xiaotong Li, Ye Sun, Jinguo Hu, Biao Hou","doi":"10.1109/tgrs.2026.3664291","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3664291","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"181 1","pages":"1-1"},"PeriodicalIF":8.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169617","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}
Pub Date : 2026-02-12DOI: 10.1109/tgrs.2026.3664116
Wei Wang, Dou Quan, Ning Huyan, Chonghua Lv, Shuang Wang, Yunan Li, Licheng Jiao
{"title":"Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration","authors":"Wei Wang, Dou Quan, Ning Huyan, Chonghua Lv, Shuang Wang, Yunan Li, Licheng Jiao","doi":"10.1109/tgrs.2026.3664116","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3664116","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"2 1","pages":"1-1"},"PeriodicalIF":8.2,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169619","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}
Aeromagnetic vector navigation has the potential to significantly improve the autonomous navigation of the aircraft, especially for low-altitude aircraft, in the environment of restricted or denied satellite positioning. Fusing geomagnetic vector measurements with inertial navigation data enables joint estimation and correction of INS error states. The key to high-precision geomagnetic vector navigation is to realize the cooperative correction of position and attitude errors of the navigation system through a geomagnetic vector navigation filter, which significantly reduces the influence of vehicle space position and attitude errors on geomagnetic vector measurement, and then obtains high-precision position and attitude estimation. However, the nonlinear and non-Gaussian characteristics are superimposed with large initial position error and magnetic measurement error, which makes it difficult to maintain high accuracy and stability of geomagnetic vector navigation filtering. To solve the above problems, a geomagnetic vector navigation strategy based on interactive marginalized particle filter (IMPF) is proposed. The two-component geomagnetic matching that is insensitive to heading errors is combined with the marginalized particle filter (MPF) framework. The MPF framework enables accurate estimation of the navigation error-state vector under nonlinear and non-Gaussian conditions. In addition, considering the comprehensive magnetic measurement error term and the two-component geomagnetic matching position constraint, the filter parameters are adaptively modified, thus improving the robustness of the geomagnetic vector navigation filter to the initial position error of the vehicle and the magnetic measurement error. The above geomagnetic vector navigation strategy can realize the cooperative optimal estimation of vehicle position and attitude under large initial position error and magnetic measurement error. The semi-measured driving simulation experiment based on the measured geomagnetic vector maps shows that this method can achieve higher accuracy and stability of geomagnetic navigation.
{"title":"Geomagnetic Vector Navigation Based on Interactive Marginalized Particle Filter","authors":"Xu Liu;Dixiang Chen;Yujing Xu;Qingfa Du;Bo Huang;Yujie Xiang;Qi Zhang;Zhongyan Liu;Mengchun Pan;Jiafei Hu;Zhuo Chen","doi":"10.1109/TGRS.2026.3664099","DOIUrl":"10.1109/TGRS.2026.3664099","url":null,"abstract":"Aeromagnetic vector navigation has the potential to significantly improve the autonomous navigation of the aircraft, especially for low-altitude aircraft, in the environment of restricted or denied satellite positioning. Fusing geomagnetic vector measurements with inertial navigation data enables joint estimation and correction of INS error states. The key to high-precision geomagnetic vector navigation is to realize the cooperative correction of position and attitude errors of the navigation system through a geomagnetic vector navigation filter, which significantly reduces the influence of vehicle space position and attitude errors on geomagnetic vector measurement, and then obtains high-precision position and attitude estimation. However, the nonlinear and non-Gaussian characteristics are superimposed with large initial position error and magnetic measurement error, which makes it difficult to maintain high accuracy and stability of geomagnetic vector navigation filtering. To solve the above problems, a geomagnetic vector navigation strategy based on interactive marginalized particle filter (IMPF) is proposed. The two-component geomagnetic matching that is insensitive to heading errors is combined with the marginalized particle filter (MPF) framework. The MPF framework enables accurate estimation of the navigation error-state vector under nonlinear and non-Gaussian conditions. In addition, considering the comprehensive magnetic measurement error term and the two-component geomagnetic matching position constraint, the filter parameters are adaptively modified, thus improving the robustness of the geomagnetic vector navigation filter to the initial position error of the vehicle and the magnetic measurement error. The above geomagnetic vector navigation strategy can realize the cooperative optimal estimation of vehicle position and attitude under large initial position error and magnetic measurement error. The semi-measured driving simulation experiment based on the measured geomagnetic vector maps shows that this method can achieve higher accuracy and stability of geomagnetic navigation.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"64 ","pages":"1-11"},"PeriodicalIF":8.6,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169618","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}
Pub Date : 2026-02-11DOI: 10.1109/tgrs.2026.3663706
Pia Addabbo, Diego Reale, Antonio Pauciullo, Gianfranco Fornaro, Danilo Orlando
{"title":"An Information-Theoretic Detector for Multiple Scatterers in SAR Tomography","authors":"Pia Addabbo, Diego Reale, Antonio Pauciullo, Gianfranco Fornaro, Danilo Orlando","doi":"10.1109/tgrs.2026.3663706","DOIUrl":"https://doi.org/10.1109/tgrs.2026.3663706","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"92 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161320","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}
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