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Recently, the application of deep learning to change detection (CD) hassignificantly progressed in remote sensing images. In recent years, CD taskshave mostly used architectures such as CNN and Transformer to identify thesechanges. However, these architectures have shortcomings in representingboundary details and are prone to false alarms and missed detections undercomplex lighting and weather conditions. For that, we propose a new network,Siamese Meets Diffusion Network (SMDNet). This network combines the Siam-U2NetFeature Differential Encoder (SU-FDE) and the denoising diffusion implicitmodel to improve the accuracy of image edge change detection and enhance themodel's robustness under environmental changes. First, we propose an innovativeSU-FDE module that utilizes shared weight features to capture differencesbetween time series images and identify similarities between features toenhance edge detail detection. Furthermore, we add an attention mechanism toidentify key coarse features to improve the model's sensitivity and accuracy.Finally, the diffusion model of progressive sampling is used to fuse key coarsefeatures, and the noise reduction ability of the diffusion model and theadvantages of capturing the probability distribution of image data are used toenhance the adaptability of the model in different environments. Our method'scombination of feature extraction and diffusion models demonstrateseffectiveness in change detection in remote sensing images. The performanceevaluation of SMDNet on LEVIR-CD, DSIFN-CD, and CDD datasets yields validatedF1 scores of 90.99%, 88.40%, and 88.47%, respectively. This substantiates theadvanced capabilities of our model in accurately identifying variations andintricate details.

Siamese Meets Diffusion Network: SMDNet for Enhanced Change Detection in High-Resolution RS Imagery