TransWCD: Scene-Adaptive Joint Constrained Framework for Weakly Supervised Change Detection

Abstract

Change detection (CD) based on deep learning typically requires costly pixel-level change labels. Recently, weakly supervised CD (WSCD) has emerged as a more label-efficient approach, using scene-level (i.e., image-level) labels to identify pixel-level changes in bitemporal images. With only scene-level labels, existing WSCD methods are typically trained as scene-level change classification models. However, these methods often suffer from label-prediction inconsistency, with false changes frequently predicted in unchanged scenes. To address this issue, we propose TransWCD-SA, an end-to-end classifier-predictor framework. TransWCD-SA consists of a hierarchical transformer-based TransWCD classifier and a scene-adaptive (SA) predictor. This classifier-predictor framework is trained with two-stage joint constraints in an end-to-end learning manner. Specifically, the TransWCD classifier integrates hierarchical transformer blocks and multiscale class activation maps (CAMs), capturing pixel-level changes across various scales under weak supervision. The SA predictor dynamically introduces different pixel-level information for scenes labeled as changed and unchanged. Furthermore, a scene gated constraint is proposed as a penalty for label-prediction inconsistency, which is activated by the Dirac delta function and rectify features of mispredicted pixels in the embedding space. We validate the effectiveness of TransWCD-SA on three datasets: Wuhan University building CD (WHU-CD), learning, vision, and remote sensing CD (LEVIR-CD), and DSIFN-CD, demonstrating significant improvement. The code is available at https://github.com/zhenghuizhao/TransWCD.