A Transformer-Based Multiscale Difference Enhancement Network for Change Detection
Change detection (CD) is an important research field in remote sensing, aimed at identifying differences in multitemporal images. Despite the progress made by convolutional neural networks and Transformer architectures in visual analysis, challenges remain in achieving robust feature representation...
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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/10876581/ |
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| author | Mengyang Pan Hang Yang Chengkang Yu Mingqing Li Anping Deng |
| author_facet | Mengyang Pan Hang Yang Chengkang Yu Mingqing Li Anping Deng |
| author_sort | Mengyang Pan |
| collection | DOAJ |
| description | Change detection (CD) is an important research field in remote sensing, aimed at identifying differences in multitemporal images. Despite the progress made by convolutional neural networks and Transformer architectures in visual analysis, challenges remain in achieving robust feature representation and global contextual understanding. To address these issues, we propose a transformer-based multiscale difference enhancement network (TMDENet). Our approach utilizes a multiscale feature extraction module to capture diverse feature representations, while incorporating a channel-spatial cooperation mechanism for refined detail enhancement. The extracted features are encoded into semantic tokens, which are processed by a Transformer to capture long-range dependencies. This is further complemented by a cross-hierarchical linear fusion module for multiscale feature integration. Additionally, a difference enhancement module isolates and emphasizes change-related features. Extensive evaluations on benchmark datasets (LEVIR, CDD, DSIFN, WHUCD) show that TMDENet achieves state-of-the-art results in boundary delineation and change localization. This study establishes TMDENet as a robust framework for high-resolution remote sensing CD, offering significant improvements in both precision and reliability. |
| format | Article |
| id | doaj-art-e08b65477c024ec2b608b476226f3de9 |
| 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-e08b65477c024ec2b608b476226f3de92025-08-20T02:08:38ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118137781379310.1109/JSTARS.2025.353941610876581A Transformer-Based Multiscale Difference Enhancement Network for Change DetectionMengyang Pan0https://orcid.org/0009-0009-9143-9759Hang Yang1https://orcid.org/0000-0001-6027-1337Chengkang Yu2https://orcid.org/0009-0006-2874-8039Mingqing Li3Anping Deng4https://orcid.org/0000-0002-0419-7176Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChange detection (CD) is an important research field in remote sensing, aimed at identifying differences in multitemporal images. Despite the progress made by convolutional neural networks and Transformer architectures in visual analysis, challenges remain in achieving robust feature representation and global contextual understanding. To address these issues, we propose a transformer-based multiscale difference enhancement network (TMDENet). Our approach utilizes a multiscale feature extraction module to capture diverse feature representations, while incorporating a channel-spatial cooperation mechanism for refined detail enhancement. The extracted features are encoded into semantic tokens, which are processed by a Transformer to capture long-range dependencies. This is further complemented by a cross-hierarchical linear fusion module for multiscale feature integration. Additionally, a difference enhancement module isolates and emphasizes change-related features. Extensive evaluations on benchmark datasets (LEVIR, CDD, DSIFN, WHUCD) show that TMDENet achieves state-of-the-art results in boundary delineation and change localization. This study establishes TMDENet as a robust framework for high-resolution remote sensing CD, offering significant improvements in both precision and reliability.https://ieeexplore.ieee.org/document/10876581/Change detection (CD)channel-spatial feature cooperation (CSFC)convolution neural network (CNN)remote sensing imageTransformer |
| spellingShingle | Mengyang Pan Hang Yang Chengkang Yu Mingqing Li Anping Deng A Transformer-Based Multiscale Difference Enhancement Network for Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection (CD) channel-spatial feature cooperation (CSFC) convolution neural network (CNN) remote sensing image Transformer |
| title | A Transformer-Based Multiscale Difference Enhancement Network for Change Detection |
| title_full | A Transformer-Based Multiscale Difference Enhancement Network for Change Detection |
| title_fullStr | A Transformer-Based Multiscale Difference Enhancement Network for Change Detection |
| title_full_unstemmed | A Transformer-Based Multiscale Difference Enhancement Network for Change Detection |
| title_short | A Transformer-Based Multiscale Difference Enhancement Network for Change Detection |
| title_sort | transformer based multiscale difference enhancement network for change detection |
| topic | Change detection (CD) channel-spatial feature cooperation (CSFC) convolution neural network (CNN) remote sensing image Transformer |
| url | https://ieeexplore.ieee.org/document/10876581/ |
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