Beyond Cross-Temporal Difference: Style-Aligned and Fusion-Difference Learning for Change Detection

Accurate change detection in bi-temporal remote sensing images remains challenging due to style differences and multi-scale target differences. Existing methods relying on cross-temporal feature comparison often suffer from style-induced false alarms. Our research proposes a Style-Aligned and Fusion-Difference Network (SAFDNet) which can distinguish style differences from genuine changes. In the encoding stage, we designed a style alignment module based on the idea of histogram equalization to normalize feature distributions of bi-temporal images after feature extraction. When calculating the difference between bi-temporal images, we optimized the existing methods from two perspectives: the object and the scale. For objects, we make full use of layer-exchange fusion mechanism: features are exchanged between temporal streams, fused within each stream, and then compared to their original versions to capture changes while mitigating style interference. For scale, we use a multi-scale Structural Similarity (SSIM) metric replaces conventional distance measures to better quantify structural differences across varying object sizes. With this design, we effectively avoid the noise effect caused by directly using the cross-temporal feature maps to calculate the changes. Our model was tested on five remote sensing datasets: WHUCD, LEVIR-CD, SYSU-CD, CDD and PX-CLCD, and all showed excellent performance. The code and pre-trained model are available at https://github.com/Vgrant0/SAFDNet.git.