Unsupervised Multimodal Change Detection by Distilling Common and Discrepant Representations

Change detection (CD) has become increasingly important in remote sensing and Earth observation. Currently, the data in various modalities has rapidly increased, and multimodal change detection is gaining prominence and holds substantial potential for applications demanding high temporal frequency or rapid response. In this research, we proposed a novel CDR-Net architecture for unsupervised multimodal change detection. The CDR-Net fuses the merits of the mask reconstruction and contrastive learning self-supervised paradigm. Within this architecture, the mask reconstruction subnetwcork pays more attention to low-level details, distilling common representations between multimodal remote sensing images to make them comparable, while the CL subnetwork emphasizes high-level semantics, extracting discrepant representations to facilitate the change detection task. Experimental results demonstrated that the CDR-Net achieved outstanding performance. This implies that the CDR-Net is of great value for resource investigation, emergency response and time-series understanding.