SCAD-Net: A Semi-Supervised Connectivity-Aware Decoupling Network for High-Resolution Remote Sensing Image Change Detection

Semi-Supervised change detection (SSCD) in remote sensing faces two critical challenges: cyclical error propagation from noisy pseudolabels and the loss of fine-grained boundary details. To address these, we propose a novel SSCD framework, the semi-supervised connectivity-aware decoupling network (SCAD-Net). SCAD-Net breaks the cycle of error amplification with a strategy called pixelsregions via curriculum-guided consistency learning (P2R-CL). This strategy progresses from initial, high-confidence pixel-level supervision to more flexible, region-based semantic correction as training matures. To tackle boundary ambiguity, SCAD-Net employs a two-stage decoupling architecture: a channel information decoupling module (CIDM) separates categorical and directional features, followed by a multiscale attention and feature integration (MAFI) module that reintegrates this information for precise boundary localization. Experiments on four benchmark datasets demonstrate our method's superiority. On the Shanghai Dataset (SHD), using only 10% labeled data, SCAD-Net achieves an F1-score of 90.1%, representing a 3.88% improvement over state-of-the-art methods. Similarly, strong performance is observed on the LEVIR-CD (90.31% F1), WHU-CD (89.35% F1), and CDD (87.05% F1) datasets. This feature decoupling paradigm offers a robust approach for semi-supervised remote sensing in environments.