BARNet: Boundary-Aware Refinement Network for Weakly Supervised Change Detection

Remote sensing change detection plays a critical role in urban land management and disaster assessment. However, most existing methods rely on expensive and time-consuming pixel-level labels, limiting their practical applicability. Weakly supervised change detection methods, such as only using image-level labels, offer the potential to reduce annotation costs while maintaining robust detection performance. However, this coarse supervisory information often makes it difficult to accurately capture fine-grained details, resulting in poor pixel-level detection accuracy. To overcome these challenges, we propose a novel Boundary-Aware Refinement Network (BARNet) for weakly supervised change detection, which utilizes a two-stage framework that first generates pixel-level pseudo labels via image-level CD activation maps, then subsequently trains a pixel-level CD network using these generated pseudo labels. Specifically, the first stage adopted a teacher-student distillation image-level CD network, which integrated a multi-scale boundary feature attention module, along with activation ambiguity loss and contrastive learning loss as feature separation constraints, to generate high-quality pseudo labels. In the second stage, these pseudo labels are used to provide deep supervision a pixel-level CD network, where the boundary-aware decoupling module further refines boundary information, leading to more precise segmentation of change areas. Extensive experiments on three public datasets demonstrate that BARNet not only achieves state-of-the-art performance in the weakly supervised change detection domain but also shows competitive performance with existing fully supervised methods, significantly reducing annotation costs while maintaining detection accuracy. With its strong performance, BARNet demonstrates great potential for practical applications in scenarios with limited supervision.