DSFI-CD: Diffusion-Guided Spatial-Frequency-Domain Information Interaction for Remote Sensing Image Change Detection

The change detection (CD) is vital in remote sensing (RS) image interpretation applications and has been well-improved driven by deep learning (DL). However, existing DL-based methods have poor model robustness due to the effect of a limited number of change samples in the existing public datasets, and on the other hand, existing most CD methods only process the input image in the spatial domain, and insufficiently process it at the frequency-domain level, which leads to its ability to obtain only shallow semantic information of the image, and insufficient ability to deal with the high-frequency information (edges and details) in the RS. In order to address the issue, this article proposed diffusion-guided spatial-frequency-domain information interaction RS CD (DSFI-CD) method, which generates part of the pseudoimages by using a conditional denoising diffusion probability model (DDPM). Furthermore, it suggests a module called spatial-frequency interaction (SFI), which combines the extraction and fusion of the corresponding spatial- and frequency-domain data with the intersection of the dual time-phase features prior to and following the change to give the model more reliable information. In addition, we designed the edge-enhanced (EE) module to further decompose the extracted features in the SFI module into boundary and body information for the change region edge information. We do this by using the concept of the flow field, and we also use the Laplace pyramid and multilevel feature fusion to increase the sensitivity of the model to the boundary while reducing the impact of noise in the nonchange region. Comprehensive experiments on four publicly accessible CD datasets show that DSFI-CD performs better than other state-of-the-art (SOTA) methods.