IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced
Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images cont...
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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/10849815/ |
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| _version_ | 1823857130182017024 |
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| author | Zhaoyang Han Linlin Zhang Qingyan Meng Chongchang Wang Wenxu Shi Maofan Zhao |
| author_facet | Zhaoyang Han Linlin Zhang Qingyan Meng Chongchang Wang Wenxu Shi Maofan Zhao |
| author_sort | Zhaoyang Han |
| collection | DOAJ |
| description | Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. The extraction of target-related features is typically difficult, and changes in scene conditions further increase the difficulty of identifying real changes. To address these challenges, we propose the interactive attention-enhanced change detection network (IAE-CDNet). We design the local–global interaction attention module, which effectively establishes the interactive relationship between local and global features and realizes information interaction between branches, enhancing the ability to obtain architectural detail features. Additionally, our change perception attention enhancement module enhances the feature perception ability of the real change area through the joint action of the internal comprehensive feature extractor and the fusion attention mechanism. We conduct extensive experiments on three datasets. Results indicate that the evaluation indicators and performance of our IAE-CDNet are better than those of other state-of-the-art methods. |
| format | Article |
| id | doaj-art-59b99c2b82c040c082c1cbbd42b9a32a |
| institution | Kabale University |
| 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-59b99c2b82c040c082c1cbbd42b9a32a2025-02-12T00:00:57ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01185063508110.1109/JSTARS.2025.353278310849815IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-EnhancedZhaoyang Han0https://orcid.org/0009-0003-6035-0504Linlin Zhang1https://orcid.org/0000-0001-5073-1694Qingyan Meng2https://orcid.org/0000-0002-5440-4081Chongchang Wang3https://orcid.org/0009-0003-6966-2360Wenxu Shi4https://orcid.org/0000-0001-6229-7995Maofan Zhao5https://orcid.org/0000-0001-6094-9128School of Geomatics, Liaoning Technical University, Fuxin, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCurrently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. The extraction of target-related features is typically difficult, and changes in scene conditions further increase the difficulty of identifying real changes. To address these challenges, we propose the interactive attention-enhanced change detection network (IAE-CDNet). We design the local–global interaction attention module, which effectively establishes the interactive relationship between local and global features and realizes information interaction between branches, enhancing the ability to obtain architectural detail features. Additionally, our change perception attention enhancement module enhances the feature perception ability of the real change area through the joint action of the internal comprehensive feature extractor and the fusion attention mechanism. We conduct extensive experiments on three datasets. Results indicate that the evaluation indicators and performance of our IAE-CDNet are better than those of other state-of-the-art methods.https://ieeexplore.ieee.org/document/10849815/Buildingschange detection (CD)high-resolution imagesinformation exchangeinteractive attention |
| spellingShingle | Zhaoyang Han Linlin Zhang Qingyan Meng Chongchang Wang Wenxu Shi Maofan Zhao IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Buildings change detection (CD) high-resolution images information exchange interactive attention |
| title | IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced |
| title_full | IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced |
| title_fullStr | IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced |
| title_full_unstemmed | IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced |
| title_short | IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced |
| title_sort | iae cdnet a remote sensing change detection network for buildings with interactive attention enhanced |
| topic | Buildings change detection (CD) high-resolution images information exchange interactive attention |
| url | https://ieeexplore.ieee.org/document/10849815/ |
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