Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification
Few-shot remote sensing scene classification has become a study that has attracted widespread attention and aims to identify new scene classes through one or a few labeled scene images. Nevertheless, due to the existence of unrelated complex background in scene images, local descriptors (LDs) that o...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10925636/ |
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| author | Anyong Qin Bin Luo Qiang Li Cuiming Zou Yu Zhao Tiecheng Song Chenqiang Gao |
| author_facet | Anyong Qin Bin Luo Qiang Li Cuiming Zou Yu Zhao Tiecheng Song Chenqiang Gao |
| author_sort | Anyong Qin |
| collection | DOAJ |
| description | Few-shot remote sensing scene classification has become a study that has attracted widespread attention and aims to identify new scene classes through one or a few labeled scene images. Nevertheless, due to the existence of unrelated complex background in scene images, local descriptors (LDs) that offer a more efficient representation than image-level features, will carry semantic information unrelated to the real semantics of the scene images. Concurrently, these irrelevant background LDs are also causing a large distribution bias in support and query sets, which leads to the problem of inaccurate feature representation of scene images. To address the aforementioned problems, in this article, we introduce an LD-based rectification network called LDRNet. Within this network, we first design an LD semantic rectification module. It performs semantic rectification on LDs that are unrelated to scene image semantics by obtaining a descriptor-level global-aware semantic representation. Second, we introduce a cross-set bias rectification module. It rectifies the query set by obtaining the offset between two sets (query and support) from a more detailed LD perspective. This operation can shorten the distance among the two sets (query and support), thereby obtaining a more accurate representation of scene image features. Furthermore, we employ an LD-based contrastive loss function to guarantee that the rectified LD semantics are consistent with the corresponding scene image. The comparative experimental result indicates that our LDRNet achieves state-of-the-art performance on three commonly used public datasets. |
| format | Article |
| id | doaj-art-292674282db4479dbc5357d5fd1c7e73 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-292674282db4479dbc5357d5fd1c7e732025-08-20T02:12:34ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189566958110.1109/JSTARS.2025.355159910925636Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene ClassificationAnyong Qin0https://orcid.org/0000-0002-2538-822XBin Luo1https://orcid.org/0009-0007-7941-2479Qiang Li2Cuiming Zou3https://orcid.org/0000-0002-2283-9048Yu Zhao4https://orcid.org/0000-0001-6593-542XTiecheng Song5https://orcid.org/0000-0003-1264-2812Chenqiang Gao6https://orcid.org/0000-0003-4174-4148School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen, ChinaFew-shot remote sensing scene classification has become a study that has attracted widespread attention and aims to identify new scene classes through one or a few labeled scene images. Nevertheless, due to the existence of unrelated complex background in scene images, local descriptors (LDs) that offer a more efficient representation than image-level features, will carry semantic information unrelated to the real semantics of the scene images. Concurrently, these irrelevant background LDs are also causing a large distribution bias in support and query sets, which leads to the problem of inaccurate feature representation of scene images. To address the aforementioned problems, in this article, we introduce an LD-based rectification network called LDRNet. Within this network, we first design an LD semantic rectification module. It performs semantic rectification on LDs that are unrelated to scene image semantics by obtaining a descriptor-level global-aware semantic representation. Second, we introduce a cross-set bias rectification module. It rectifies the query set by obtaining the offset between two sets (query and support) from a more detailed LD perspective. This operation can shorten the distance among the two sets (query and support), thereby obtaining a more accurate representation of scene image features. Furthermore, we employ an LD-based contrastive loss function to guarantee that the rectified LD semantics are consistent with the corresponding scene image. The comparative experimental result indicates that our LDRNet achieves state-of-the-art performance on three commonly used public datasets.https://ieeexplore.ieee.org/document/10925636/Bias rectificationfew-shot learning (FSL)local descriptor (LD)remote sensing scene classification (RSSC)semantics rectification |
| spellingShingle | Anyong Qin Bin Luo Qiang Li Cuiming Zou Yu Zhao Tiecheng Song Chenqiang Gao Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bias rectification few-shot learning (FSL) local descriptor (LD) remote sensing scene classification (RSSC) semantics rectification |
| title | Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification |
| title_full | Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification |
| title_fullStr | Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification |
| title_full_unstemmed | Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification |
| title_short | Local-Descriptors-Based Rectification Network for Few-Shot Remote Sensing Scene Classification |
| title_sort | local descriptors based rectification network for few shot remote sensing scene classification |
| topic | Bias rectification few-shot learning (FSL) local descriptor (LD) remote sensing scene classification (RSSC) semantics rectification |
| url | https://ieeexplore.ieee.org/document/10925636/ |
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