HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916699697053696 |
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| author | Han, Chengxi Su, Xiaoyu Wei, Zhiqiang Hu, Meiqi Xu, Yichu |
| author_facet | Han, Chengxi Su, Xiaoyu Wei, Zhiqiang Hu, Meiqi Xu, Yichu |
| contents | The remote sensing image change detection task is an essential method for large-scale monitoring. We propose HSANet, a network that uses hierarchical convolution to extract multi-scale features. It incorporates hybrid self-attention and cross-attention mechanisms to learn and fuse global and cross-scale information. This enables HSANet to capture global context at different scales and integrate cross-scale features, refining edge details and improving detection performance. We will also open-source our model code: https://github.com/ChengxiHAN/HSANet. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection Han, Chengxi Su, Xiaoyu Wei, Zhiqiang Hu, Meiqi Xu, Yichu Computer Vision and Pattern Recognition The remote sensing image change detection task is an essential method for large-scale monitoring. We propose HSANet, a network that uses hierarchical convolution to extract multi-scale features. It incorporates hybrid self-attention and cross-attention mechanisms to learn and fuse global and cross-scale information. This enables HSANet to capture global context at different scales and integrate cross-scale features, refining edge details and improving detection performance. We will also open-source our model code: https://github.com/ChengxiHAN/HSANet. |
| title | HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15170 |