Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection
Change Detection (CD) is a fundamental task in remote sensing image analysis, widely applied in fields such as municipal planning and vital signs monitoring. However, many existing methods struggle to extract change-relevant features when faced with dual-temporal remote sensing images characterized...
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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/11006998/ |
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| author | Canbin Hu Sida Du Hongyun Chen Xiaokun Sun Kailun Liu |
| author_facet | Canbin Hu Sida Du Hongyun Chen Xiaokun Sun Kailun Liu |
| author_sort | Canbin Hu |
| collection | DOAJ |
| description | Change Detection (CD) is a fundamental task in remote sensing image analysis, widely applied in fields such as municipal planning and vital signs monitoring. However, many existing methods struggle to extract change-relevant features when faced with dual-temporal remote sensing images characterized by inconsistent and complex feature distributions, leading to false alarms. Moreover, these methods rely on simple concatenation, differential operations, or the addition of attention mechanisms to fuse dual-temporal features, resulting in the loss of edge information. Additionally, these fusion strategies fail to fully exploit the relationship between dual-temporal features and difference features, which negatively impacts the overall performance of CD. Furthermore, the problem of internal holes remains unresolved. To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). This network effectively establishes interaction between dual-temporal features through difference interaction attention module (DIAM), enhancing the capability to extract change features. During the feature fusion stage, the edge-enhanced difference fusion module (EEDFM) thoroughly integrates dual-temporal features with difference features, and employs an edge enhancement mechanism to prevent the loss of edge information. In the decoding stage, multi-scale change guidance module (MSCGM) combines prior change information with enhanced multi-scale dilated convolutions, addressing the limited receptive field of traditional CNN decoders, thereby effectively mitigating the internal holes. Experimental results on three public datasets demonstrate that CGDIANet outperforms ten existing advanced methods. |
| format | Article |
| id | doaj-art-2c5eb0d65c5c4e84bae591cacdeb2768 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2c5eb0d65c5c4e84bae591cacdeb27682025-08-20T02:29:15ZengIEEEIEEE Access2169-35362025-01-0113896548966610.1109/ACCESS.2025.357146311006998Change-Guided Difference Interaction Attention Network for Remote Sensing Change DetectionCanbin Hu0https://orcid.org/0000-0002-4183-5106Sida Du1Hongyun Chen2Xiaokun Sun3Kailun Liu4College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaCollege of Information Science and Technology, Beijing University of Chemical Technology, Beijing, ChinaChange Detection (CD) is a fundamental task in remote sensing image analysis, widely applied in fields such as municipal planning and vital signs monitoring. However, many existing methods struggle to extract change-relevant features when faced with dual-temporal remote sensing images characterized by inconsistent and complex feature distributions, leading to false alarms. Moreover, these methods rely on simple concatenation, differential operations, or the addition of attention mechanisms to fuse dual-temporal features, resulting in the loss of edge information. Additionally, these fusion strategies fail to fully exploit the relationship between dual-temporal features and difference features, which negatively impacts the overall performance of CD. Furthermore, the problem of internal holes remains unresolved. To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). This network effectively establishes interaction between dual-temporal features through difference interaction attention module (DIAM), enhancing the capability to extract change features. During the feature fusion stage, the edge-enhanced difference fusion module (EEDFM) thoroughly integrates dual-temporal features with difference features, and employs an edge enhancement mechanism to prevent the loss of edge information. In the decoding stage, multi-scale change guidance module (MSCGM) combines prior change information with enhanced multi-scale dilated convolutions, addressing the limited receptive field of traditional CNN decoders, thereby effectively mitigating the internal holes. Experimental results on three public datasets demonstrate that CGDIANet outperforms ten existing advanced methods.https://ieeexplore.ieee.org/document/11006998/Change detectiondeep learningdifference featuresedge enhancementinteractive attentionprior change information |
| spellingShingle | Canbin Hu Sida Du Hongyun Chen Xiaokun Sun Kailun Liu Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection IEEE Access Change detection deep learning difference features edge enhancement interactive attention prior change information |
| title | Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection |
| title_full | Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection |
| title_fullStr | Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection |
| title_full_unstemmed | Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection |
| title_short | Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection |
| title_sort | change guided difference interaction attention network for remote sensing change detection |
| topic | Change detection deep learning difference features edge enhancement interactive attention prior change information |
| url | https://ieeexplore.ieee.org/document/11006998/ |
| work_keys_str_mv | AT canbinhu changeguideddifferenceinteractionattentionnetworkforremotesensingchangedetection AT sidadu changeguideddifferenceinteractionattentionnetworkforremotesensingchangedetection AT hongyunchen changeguideddifferenceinteractionattentionnetworkforremotesensingchangedetection AT xiaokunsun changeguideddifferenceinteractionattentionnetworkforremotesensingchangedetection AT kailunliu changeguideddifferenceinteractionattentionnetworkforremotesensingchangedetection |