Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation

The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. However, these methods primarily focus on utilizing unlabeled data through various training strategies, neglecting the impact of pseudo-changes and learning bias in models. When dealing with limited labeled data, abundant low-quality pseudo-labels generated by poorly performing models can hinder effective performance improvement, leading to the incomplete recognition results of changes to buildings. To address this issue, we propose a feature multi-scale information interaction and complementation semi-supervised method based on consistency regularization (MSFG-SemiCD), which includes a multi-scale feature fusion-guided change detection network (MSFGNet) and a semi-supervised update method. Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. Moreover, this enables the fusion and complementation of information between features, resulting in more complete change features. The semi-supervised update method employs a weak-to-strong consistency framework to achieve model parameter updates while maintaining perturbation invariance of unlabeled data at both input and encoder output features. Experimental results on the WHU-CD and LEVIR-CD datasets confirm the efficacy of the proposed method. There is a notable improvement in performance at both the 1% and 5% levels. The IOU in the WHU-CD dataset increased by 5.72% and 6.84%, respectively, while in the LEVIR-CD dataset, it improved by 18.44% and 5.52%, respectively.