Dynamically updated semi-supervised change detection network combining cross-supervision and screening algorithms

Semi-supervised change detection is increasingly becoming an interesting and challenging topic for the remote sensing image processing community. As the application of deep learning in change detection becomes more and more widespread, there is a growing lack of labeled training data, which substantially limits the practical application of change detection. In order to discuss a more effective semi-supervised change detection approach and to make more reasonable use of the large amount of remote sensing data, we propose a semi-supervised change detection framework in this paper, which utilizes two different networks to cross-supervise and provide information to each other. Unlike most existing semi-supervised change detection, the proposed framework also incorporates a new filtering algorithm to find better pseudo-labels for the retraining of the two networks in the paper. Then, the computation of the loss functions of the two networks is crossed and the two networks are used for Transformer and CNN different learning paradigms, respectively, while simplifying the classical deep collaborative learning for consistency regularization. In addition, we add two markers to record the highest MIoU of training during retraining, and dynamically update the pseudo-labels as the training metrics progressively improve, which significantly improves the training effect. Our approach is tested on public dataset and achieves very good results that effectively demonstrate the effectiveness of the proposed framework.