SemiJointNet: A Semi-Supervised Building Change Detection Method Based On Joint Learning

Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a single-temporal semi-supervised joint learning framework for building change detection, called SemiJointNet. Firstly, to reduce annotation costs, SemiJointNet uses building extraction labels instead of change detection labels to train the change detection model. Furthermore, to improve the semantic understanding capability of the model, SemiJointNet employs a joint learning approach for building extraction and change detection tasks. Lastly, SemiJointNet introduces semi-supervised learning, reducing the need for labels from thousands to dozens. Experimental results on the WHU dataset demonstrate that the proposed SemiJointNet achieves excellent building change detection performance with only a few dozen labels.