CSNet: Change Selection of Activations and Pseudomasks for Image-Level Weakly Supervised Change Detection

Weakly supervised change detection (WSCD) of bitemporal remote sensing (RS) images has gained attention for its ability to reduce reliance on labor-intensive pixel-level change masks. Recent methods leverage image-level weak supervision to generate change pseudomasks for detecting changed objects, typically using the class activation map (CAM) technique combined with DenseCRF or the segment anything model (SAM). However, these methods still face two main challenges: first, CAMs tend to produce weak or false activations for changed objects, and second, DenseCRF and SAM lead to unreliable pseudomask generation, particularly when complex variations occur within objects in bitemporal images. To address these challenges, a change selection network (CSNet) is proposed to enhance the quality of change activation maps and pseudomasks, improving their ability to accurately extract changed regions in bitemporal RS images. First, a change activation selection (CAS) module is designed to generate a weight mask that selects and aggregates change-representing features, effectively highlighting missed change activations and strengthening weak activations. Second, a bitemporal image selection (BIS) strategy is developed, incorporating two selection rules to filter out image pairs with a poor-quality mask derived from SAM while retaining those with high-quality results. Finally, a change pseudomask generation (CPG) module integrated with an atrous spatial pyramid pooling (ASPP) classifier is developed to predict accurate change pixels for final pseudomask generation. Experimental results demonstrate that the proposed CSNet outperforms existing WSCD methods, achieving 79.32% IoU in change pseudomasks for the WHU-CD dataset, 68.12% for the GZ-CD dataset, and 75.44% for the GVLM dataset. This study proposes a novel method that enhances the performance of the weakly supervised paradigm in RS CD.