CINet: A Constraint- and Interaction-Based Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module.