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In recent years, the application of deep learning to change detection (CD) has significantly progressed in remote sensing images. CD tasks have mostly used architectures, such as CNN and Transformer to locate image changes. However, these architectures have shortcomings in representing boundary details and are prone to false alarms and missed detections under complex lighting and weather conditions. For that, we propose a new network, Siamese meets diffusion network (SMDNet), a CD model that combines discriminative and generative architecture. By leveraging the power of the Siam-U2Net feature differential encoder (SU-FDE) and denoising diffusion implicit model (DDIM), it not only improves the accuracy of object edge detection but also enhances the data through iterative denoising and thinning reconstruction detail detection accuracy. Improves the model's robustness under environmental …