CSDACD: Domain-adaptive Change Detection Network for Cross-seasonal Remote Sensing Images

Change detection (CD) is a crucial task in earth remote sensing, and it plays a significant role in analyzing changes in a region. However, bi-temporal images acquisition may cause domain shift problems due to the seasonal difference. Most cross-seasonal change detection methods currently in use make it difficult to capture the impact of domain shift. In contrast, domain adaptation (DA) methods struggle with fuzzy semantic correspondence and suboptimal use of generated data, which leads to incomplete target images. To address these problems, we propose CSDACD, an end-to-end domain-adaptive change detection network that integrates domain-adaptive images into the CD network. Additionally, we introduce the Semantic Change Alignment (SCA) module to include the registration and semantic change information of bi-temporal images and design three data fusion modules to enhance the adaptability of DA and CD networks. Our experiments show that CSDACD outperforms the state-of-the-art (SOTA) methods in cross-seasonal change detection tasks, achieving an F1-score of 98.73% and IoU of 97.49% in the CDD dataset. Source code and models are available at https://github.com/24kironhead/CSDACD.