Semi-Supervised Change Detection With Boundary Refinement Teacher

High-quality pseudo-labels are critical for guiding learning in semi-supervised change detection (SSCD). Recently, many SSCD methods based on consistency regularization (CR) have achieved advanced performance. These methods typically generate pseudo-labels by setting a fixed threshold. However, this strategy struggles to generate pseudo-labels with high-quality boundary details. To this end, we propose a novel SSCD method boundary refinement teacher (BRT) to enhance the boundary quality of pseudo-labels. A bi-temporal image boundary refinement (BIBR) module is designed to uncover boundary details in unlabeled images at first. BIBR explores the boundary characteristics of pseudo-label by extracting the boundary blocks from its change map and re-delineates the boundary decision points with their magnified views. Then, a stable teacher parameter update (STPU) module is devised to sustain the semi-supervised learning state steady, avoiding frequent updates to the teacher model parameters. These stable updates to teacher model parameters provide continuous and high-quality guidance, preventing pseudo-label fluctuations from disrupting the student model’s acquisition of new knowledge. Extensive experiments are conducted on three commonly used change detection datasets, encompassing buildings and multiple categories, covering SSCD settings, boundary metrics, and detailed ablation studies. Our results show that simply enhancing the boundary quality of the pseudo-labels allows BRT to consistently deliver state-of-the-art (SOTA) performance in SSCD. The code is available at: https://github.com/yoghurts-sy/BRT.