CBSASNet: A Siamese Network Based on Channel Bias Split Attention for Remote Sensing Change Detection
Naiwei He, Liejun Wang, Panpan Zheng, Cui Zhang, Lele Li
Abstract
Remote sensing image change detection (CD) is an important technology for monitoring ground object change. Although Transformer-based CD methods have been proposed and achieved good results, however, there does exist one open problem: Transformer-based methods are weak for localizing information acquisition, easily ignore detailed information, and are of high computational complexity. Also, the variation of target sizes challenges the generalization of networks. To address these issues, we propose a siamese network named as CBSASNet for remote sensing CD, in which channel bias split attention is employed to recover the information in the change region and the cross-temporal fusion module is utilized to highlight the information of change regions through the optimized single-temporal image features. The experimental result indicates that CBSASNet does not only outperform 16 state-of-the-art works, but also its modules complement each other in the ablation testing.