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.

翻译：近年来，深度学习在遥感影像变化检测领域取得了显著进展。目前多数变化检测任务采用CNN与Transformer等架构识别变化区域，但这类架构在边界细节表征方面存在不足，且在复杂光照和天气条件下易产生虚警与漏检。为此，本文提出新型网络SMDNet（Siamese Meets Diffusion Network）。该网络融合Siam-U2Net特征差分编码器（SU-FDE）与去噪扩散隐式模型，旨在提升影像边缘变化检测精度并增强模型对环境变化的鲁棒性。首先，我们创新性地提出SU-FDE模块，通过共享权重特征捕捉时序影像差异，并利用特征相似性识别以增强边缘细节检测。其次，引入注意力机制识别关键粗粒度特征，提升模型敏感度与准确性。最后，采用渐进采样扩散模型融合关键粗粒度特征，利用扩散模型的降噪能力及其对影像数据概率分布的捕获优势，增强模型在不同环境中的适应性。本文提出的特征提取与扩散模型融合方法在遥感影像变化检测中展现出有效性。在LEVIR-CD、DSIFN-CD与CDD数据集上的性能评估显示，SMDNet的验证F1分数分别达到90.99%、88.40%与88.47%，验证了模型在精准识别变化区域与复杂细节方面的先进能力。