Adaptive Differentiation Siamese Fusion Network for Remote Sensing Change Detection

Remote sensing images' change detection is crucial for disaster monitoring, urban planning, and environmental surveillance. Despite recent advancements in deep learning enhancing change detection methods, challenges persist in detecting small object changes and distinguishing pseudochanges. To address these issues, we propose the adaptive differentiation Siamese fusion network (ADSFNet). ADSFNet features a Siamese encoder-decoder architecture designed to overcome these challenges. The innovative Siamese encoder replaces traditional self-attention with dilated neighborhood attention, enhancing the detection of small objects. In addition, the specific feature enhancer (SFE) captures differences between bitemporal features, improving performance when pseudochanges are present. The multilevel differential feature adaptive fusion module (MDFAFM) integrates differential features across various levels, facilitating the prediction of accurate change maps. Experimental results on benchmark datasets demonstrate that the proposed ADSFNet significantly outperforms existing state-of-the-art (SOTA) methods in accuracy.