  Recently, the application of deep learning to change detection (CD) has
significantly progressed in remote sensing images. In recent years, CD tasks
have mostly used architectures such as CNN and Transformer to identify these
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). This network combines the Siam-U2Net
Feature Differential Encoder (SU-FDE) and the denoising diffusion implicit
model to improve the accuracy of image edge change detection and enhance the
model's robustness under environmental changes. First, we propose an innovative
SU-FDE module that utilizes shared weight features to capture differences
between time series images and identify similarities between features to
enhance edge detail detection. Furthermore, we add an attention mechanism to
identify key coarse features to improve the model's sensitivity and accuracy.
Finally, the diffusion model of progressive sampling is used to fuse key coarse
features, and the noise reduction ability of the diffusion model and the
advantages of capturing the probability distribution of image data are used to
enhance the adaptability of the model in different environments. Our method's
combination of feature extraction and diffusion models demonstrates
effectiveness in change detection in remote sensing images. The performance
evaluation of SMDNet on LEVIR-CD, DSIFN-CD, and CDD datasets yields validated
F1 scores of 90.99%, 88.40%, and 88.47%, respectively. This substantiates the
advanced capabilities of our model in accurately identifying variations and
intricate details.
