Papers › HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection
HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection
Chengxi Han, Xiaoyu Su, Zhiqiang Wei, Meiqi Hu, Yichu Xu
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.
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