MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection
Remote sensing (RS) image change detection (CD) aims to identify and localize the differences in the same geographical area observed across bitemporal RS images. Existing CD methods typically focus on characterizing and distinguishing change regions, while often overlooking the nonchange areas (back...
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| Format: | Article |
| Language: | English |
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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/10972294/ |
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| author | Andong Huang Chuan Xu Liye Mei Zhiwei Ye Wei Yang Ying Wang Xinghua Li |
| author_facet | Andong Huang Chuan Xu Liye Mei Zhiwei Ye Wei Yang Ying Wang Xinghua Li |
| author_sort | Andong Huang |
| collection | DOAJ |
| description | Remote sensing (RS) image change detection (CD) aims to identify and localize the differences in the same geographical area observed across bitemporal RS images. Existing CD methods typically focus on characterizing and distinguishing change regions, while often overlooking the nonchange areas (background information) in RS images. Therefore, the influence of subtle changes in nonchange regions may be amplified, leading to false negatives or false positives in the change regions. To address this issue, we propose a multiview complementary learning network (MVCL) for RS image CD, which considers CD from both background view and change view perspectives, while focusing on the extraction and aggregation of both change and nonchange (background) features. Specifically, we introduce a background branch during the encoder stage dedicated to extracting background information. This background information is then injected into the change regions through the background-dependent feature fusion module, serving as prior knowledge. By leveraging the stable background information, irrelevant and noisy changes are suppressed, guiding the generation of change regions and reducing the interference of background noise. Furthermore, to mitigate the noise introduced during multiscale fusion in the nonchange (background) areas, we propose a prior-guided spatial background enhancement module, which consists of a prior semantic guidance strategy and a spatial background enhancement module. According to the results of experiments conducted on three public datasets, the proposed method outperforms several state-of-the-art (SOTA) methods in both quantitative metrics and visual effectiveness. |
| format | Article |
| id | doaj-art-ea9d041a094e405cb15e29925febf7db |
| 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-ea9d041a094e405cb15e29925febf7db2025-08-20T01:56:48ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118128311284410.1109/JSTARS.2025.356165610972294MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change DetectionAndong Huang0https://orcid.org/0009-0007-0644-7080Chuan Xu1https://orcid.org/0000-0002-7099-7833Liye Mei2https://orcid.org/0000-0002-2555-9199Zhiwei Ye3https://orcid.org/0000-0002-1218-0681Wei Yang4https://orcid.org/0000-0002-2014-8120Ying Wang5https://orcid.org/0009-0007-3655-9702Xinghua Li6https://orcid.org/0000-0002-2094-6480School of Information Science and Engineering, Wuchang Shouyi University, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Computer Science, Hubei University of Technology, Wuhan, ChinaSchool of Information Science and Engineering, Wuchang Shouyi University, Wuhan, ChinaSchool of Information Science and Engineering, Wuchang Shouyi University, Wuhan, ChinaSchool of Remote Sensing Information Engineering, Wuhan University, Wuhan, ChinaRemote sensing (RS) image change detection (CD) aims to identify and localize the differences in the same geographical area observed across bitemporal RS images. Existing CD methods typically focus on characterizing and distinguishing change regions, while often overlooking the nonchange areas (background information) in RS images. Therefore, the influence of subtle changes in nonchange regions may be amplified, leading to false negatives or false positives in the change regions. To address this issue, we propose a multiview complementary learning network (MVCL) for RS image CD, which considers CD from both background view and change view perspectives, while focusing on the extraction and aggregation of both change and nonchange (background) features. Specifically, we introduce a background branch during the encoder stage dedicated to extracting background information. This background information is then injected into the change regions through the background-dependent feature fusion module, serving as prior knowledge. By leveraging the stable background information, irrelevant and noisy changes are suppressed, guiding the generation of change regions and reducing the interference of background noise. Furthermore, to mitigate the noise introduced during multiscale fusion in the nonchange (background) areas, we propose a prior-guided spatial background enhancement module, which consists of a prior semantic guidance strategy and a spatial background enhancement module. According to the results of experiments conducted on three public datasets, the proposed method outperforms several state-of-the-art (SOTA) methods in both quantitative metrics and visual effectiveness.https://ieeexplore.ieee.org/document/10972294/Additional branchchange detection (CD)prior-guidedremote sensing (RS)spatial background enhancement |
| spellingShingle | Andong Huang Chuan Xu Liye Mei Zhiwei Ye Wei Yang Ying Wang Xinghua Li MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Additional branch change detection (CD) prior-guided remote sensing (RS) spatial background enhancement |
| title | MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection |
| title_full | MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection |
| title_fullStr | MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection |
| title_full_unstemmed | MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection |
| title_short | MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection |
| title_sort | mvcl multiview complementary learning network for remote sensing image change detection |
| topic | Additional branch change detection (CD) prior-guided remote sensing (RS) spatial background enhancement |
| url | https://ieeexplore.ieee.org/document/10972294/ |
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