Dual Consistency Learning for Weakly Supervised Change Detection With SAM-Based Priors

Abstract

Most existing change detection methods rely on supervised learning and require costly pixel-level annotations. Weakly supervised change detection (WSCD) alleviates this burden by using only image-level labels. Existing image-level WSCD methods usually generate class activation maps (CAMs) through change classification and then obtain change maps by thresholding the CAMs. However, CAMs naturally highlight the most discriminative regions, which leads to a lack of sufficient spatial details. In addition, the appearance heterogeneity of land-cover objects easily causes class ambiguity in CAMs. To address these issues, we propose dual consistency learning (DUEL). DUEL introduces two consistency constraints, namely feature consistency and activation consistency, both guided by segment anything model (SAM)-derived object masks. Feature consistency uses mask-derived pairwise relation labels to perform semantic alignment in the bitemporal feature space, which helps alleviate class ambiguity. Activation consistency combines semantic cues from CAMs with spatial priors from SAM masks to generate pseudolabels for CAM regularization, with the goal of better recovering spatial details. Extensive experiments on two public change detection datasets demonstrate that DUEL outperforms several state-of-the-art WSCD methods.