CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection
Remote sensing change detection (RSCD) is essential for monitoring land use, urban expansion, and environmental changes. Despite advancements in deep learning, existing methods exhibit limitations such as discontinuous edges and internal gaps in detected change regions, primarily due to insufficient...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10964253/ |
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| author | Jiting Zhou Pu Zhang Zhihao Zhou |
| author_facet | Jiting Zhou Pu Zhang Zhihao Zhou |
| author_sort | Jiting Zhou |
| collection | DOAJ |
| description | Remote sensing change detection (RSCD) is essential for monitoring land use, urban expansion, and environmental changes. Despite advancements in deep learning, existing methods exhibit limitations such as discontinuous edges and internal gaps in detected change regions, primarily due to insufficient feature fusion and limited contextual understanding. In this paper, we propose a novel change indicator-enhanced multiscale fusion network, called CIMF-Net, which integrates multiscale feature fusion modules (MFFM) and cascade guided attention modules (CGAM) to overcome these limitations. MFFM facilitates the extraction of semantic information through multiscale features, while CGAM employs hierarchical attention mechanisms to refine feature representation and enhance change region localization. Extensive experiments on three benchmark datasets highlight the superior performance of CIMF-Net compared to baseline network, achieving higher F1-scores, IoU, and overall accuracy. Furthermore, ablation studies confirm the effectiveness of MFFM and CGAM in enhancing feature fusion and hierarchical guidance. Our method not only delivers robust results in challenging scenarios but also presents a new perspective and technology for change detection. |
| format | Article |
| id | doaj-art-a22797fc95b44b26a71185d03b7b058b |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a22797fc95b44b26a71185d03b7b058b2025-08-20T02:18:24ZengIEEEIEEE Access2169-35362025-01-0113668436685410.1109/ACCESS.2025.356059110964253CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change DetectionJiting Zhou0https://orcid.org/0000-0001-7534-9993Pu Zhang1https://orcid.org/0009-0004-2429-4637Zhihao Zhou2https://orcid.org/0009-0008-9124-4364Shanghai University, Shanghai, ChinaShanghai University, Shanghai, ChinaShanghai University, Shanghai, ChinaRemote sensing change detection (RSCD) is essential for monitoring land use, urban expansion, and environmental changes. Despite advancements in deep learning, existing methods exhibit limitations such as discontinuous edges and internal gaps in detected change regions, primarily due to insufficient feature fusion and limited contextual understanding. In this paper, we propose a novel change indicator-enhanced multiscale fusion network, called CIMF-Net, which integrates multiscale feature fusion modules (MFFM) and cascade guided attention modules (CGAM) to overcome these limitations. MFFM facilitates the extraction of semantic information through multiscale features, while CGAM employs hierarchical attention mechanisms to refine feature representation and enhance change region localization. Extensive experiments on three benchmark datasets highlight the superior performance of CIMF-Net compared to baseline network, achieving higher F1-scores, IoU, and overall accuracy. Furthermore, ablation studies confirm the effectiveness of MFFM and CGAM in enhancing feature fusion and hierarchical guidance. Our method not only delivers robust results in challenging scenarios but also presents a new perspective and technology for change detection.https://ieeexplore.ieee.org/document/10964253/Remote sensing change detectionfeature fusionattention mechanismdeep learning |
| spellingShingle | Jiting Zhou Pu Zhang Zhihao Zhou CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection IEEE Access Remote sensing change detection feature fusion attention mechanism deep learning |
| title | CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection |
| title_full | CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection |
| title_fullStr | CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection |
| title_full_unstemmed | CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection |
| title_short | CIMF-Net: A Change Indicator-Enhanced Multiscale Fusion Network for Remote Sensing Change Detection |
| title_sort | cimf net a change indicator enhanced multiscale fusion network for remote sensing change detection |
| topic | Remote sensing change detection feature fusion attention mechanism deep learning |
| url | https://ieeexplore.ieee.org/document/10964253/ |
| work_keys_str_mv | AT jitingzhou cimfnetachangeindicatorenhancedmultiscalefusionnetworkforremotesensingchangedetection AT puzhang cimfnetachangeindicatorenhancedmultiscalefusionnetworkforremotesensingchangedetection AT zhihaozhou cimfnetachangeindicatorenhancedmultiscalefusionnetworkforremotesensingchangedetection |