Siamese change detection based on information interaction and fusion network
Abstract Change detection is widely utilized across various domains, such as disaster monitoring, where it aids in identifying differences between images captured at different time intervals. However, current methods often lack constraints on intermediate features and fail to comprehensively model t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15468-w |
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| author | Yanni Zhang Lei Yang Caigen Zhou Jiachen Wen Licai Zhu |
| author_facet | Yanni Zhang Lei Yang Caigen Zhou Jiachen Wen Licai Zhu |
| author_sort | Yanni Zhang |
| collection | DOAJ |
| description | Abstract Change detection is widely utilized across various domains, such as disaster monitoring, where it aids in identifying differences between images captured at different time intervals. However, current methods often lack constraints on intermediate features and fail to comprehensively model the temporal relationships among these features. Additionally, they rely on simplistic fusion mechanisms, leading to suboptimal network performance. In this paper, we propose: (1) a Feature Information Interaction Module (FIIM) based on spatial attention to enhance semantic information; (2) a Feature Pair Fusion Module (FPFM) with dual-branch structure to model bi-temporal relationships; and (3) a Multi-Scale Supervision Method (MSSM) using contrastive learning to better constrain intermediate features. Comparative experiments conducted on the CDD and LEVIR-CD datasets demonstrate the superiority of our proposed network over existing state-of-the-art methods. Code repository: https://github.com/joyeuxni/SNIIF-Net . |
| format | Article |
| id | doaj-art-52d6bf6aaf5841f7b7d9b575f5c008b6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-52d6bf6aaf5841f7b7d9b575f5c008b62025-08-20T03:45:56ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-15468-wSiamese change detection based on information interaction and fusion networkYanni Zhang0Lei Yang1Caigen Zhou2Jiachen Wen3Licai Zhu4School of Artificial Intelligence, Yancheng Teachers UniversitySchool of Artificial Intelligence, Yancheng Teachers UniversitySchool of Artificial Intelligence, Yancheng Teachers UniversitySchool of Artificial Intelligence, Yancheng Teachers UniversitySchool of Artificial Intelligence, Yancheng Teachers UniversityAbstract Change detection is widely utilized across various domains, such as disaster monitoring, where it aids in identifying differences between images captured at different time intervals. However, current methods often lack constraints on intermediate features and fail to comprehensively model the temporal relationships among these features. Additionally, they rely on simplistic fusion mechanisms, leading to suboptimal network performance. In this paper, we propose: (1) a Feature Information Interaction Module (FIIM) based on spatial attention to enhance semantic information; (2) a Feature Pair Fusion Module (FPFM) with dual-branch structure to model bi-temporal relationships; and (3) a Multi-Scale Supervision Method (MSSM) using contrastive learning to better constrain intermediate features. Comparative experiments conducted on the CDD and LEVIR-CD datasets demonstrate the superiority of our proposed network over existing state-of-the-art methods. Code repository: https://github.com/joyeuxni/SNIIF-Net .https://doi.org/10.1038/s41598-025-15468-wChange detectionInformation interactionFeature fusionContrastive learning |
| spellingShingle | Yanni Zhang Lei Yang Caigen Zhou Jiachen Wen Licai Zhu Siamese change detection based on information interaction and fusion network Scientific Reports Change detection Information interaction Feature fusion Contrastive learning |
| title | Siamese change detection based on information interaction and fusion network |
| title_full | Siamese change detection based on information interaction and fusion network |
| title_fullStr | Siamese change detection based on information interaction and fusion network |
| title_full_unstemmed | Siamese change detection based on information interaction and fusion network |
| title_short | Siamese change detection based on information interaction and fusion network |
| title_sort | siamese change detection based on information interaction and fusion network |
| topic | Change detection Information interaction Feature fusion Contrastive learning |
| url | https://doi.org/10.1038/s41598-025-15468-w |
| work_keys_str_mv | AT yannizhang siamesechangedetectionbasedoninformationinteractionandfusionnetwork AT leiyang siamesechangedetectionbasedoninformationinteractionandfusionnetwork AT caigenzhou siamesechangedetectionbasedoninformationinteractionandfusionnetwork AT jiachenwen siamesechangedetectionbasedoninformationinteractionandfusionnetwork AT licaizhu siamesechangedetectionbasedoninformationinteractionandfusionnetwork |