Progressive Pseudo-Label Optimization for Point-Supervised Change Detection

2026-09-02

AI Summary

该研究针对点监督变化检测中伪标签不完整和噪声问题，提出两阶段框架结合SAM2先验逐步优化伪标签，提高边界质量和结构一致性。

Abstract

Point-supervised change detection (PS-CD) aims to identify pixel-level changes between bi-temporal images using only sparsely annotated points. Although point annotations substantially reduce labeling costs, their limited spatial coverage often results in incomplete and noisy pseudo-labels. To address this issue, we propose a two-stage framework that introduces SAM2 priors into PS-CD and progressively adapts them to the target task. In Stage I, SAM2 generates object-aware candidate masks from point annotations on the bi-temporal images, and a bi-temporal mask selection strategy is designed to convert generic segmentation responses into more reliable change pseudo-labels. Subsequently, a lightweight CNN refinement module with an uncertainty-aware loss is employed to improve boundary quality and local structural consistency. In Stage II, we construct a teacher-student self-training framework in which the teacher is updated by exponential moving average and periodically refreshes the pseudo-labels. This design establishes a closed-loop optimization process that alternates between pseudo-label refinement and model re-optimization. Experiments on three benchmark datasets, including WHU-CD, LEVIR-CD, and SYSU-CD, demonstrate that the proposed method outperforms previous weakly supervised approaches on most benchmarks and remains competitive with several fully supervised methods.

Problem

• Point-supervised change detection
• Sparse annotations
• Pseudo-labels
• Bi-temporal images

Innovation

• two-stage framework
• SAM2 priors
• uncertainty-aware loss
• teacher-student self-training