Multiscale Self-Supervised Constraints and Change-Masks-Guided Network for Weakly Supervised Change Detection

Jia Liu, Hejun Luo, Wenhua Zhang, Fang Liu, Liang Xiao

Abstract: Remote sensing change detection (CD) is a highly significant subtask within the field of Earth observation. Recently, weakly supervised CD (WSCD) methods based on image-level annotations have attracted interest, it is challenging to generate a clear margin between changed and unchanged regions with a lack of detailed annotation. In this article, based on class activation maps (CAMs), we propose a novel WSCD network based on self-supervised learning and change mask guidance (SSCMNet). First, we design a multiscale self-supervised constraint (MSC) module to narrow the gap between weak supervision and full supervision and compensate for the inherent shortcomings of CAMs. Second, a change mask guidance (CMG) module is proposed to further guide the network to keep the integrity of changed objects according to the consistency within unchanged regions and inconsistency within changed regions. Finally, to address the challenge of transferring commonly used post-processing methods in semantic segmentation to CD, an adaptive post-processing (APP) module is designed to adaptively select one of the input images for post-processing. We conduct experiments on three publicly available remote sensing CD datasets. Quantitative metrics and visualized results demonstrate the outstanding performance of the proposed method.

External IDs:doi:10.1109/tgrs.2025.3539630