Reliable Contrastive Learning for Semi-supervised Change Detection in Remote Sensing Images

With the development of deep learning in remote sensing image change detection, the dependence of change detection models on labeled data has become an important problem. To make better use of the comparatively resource-saving unlabeled data, the change detection method based on semi-supervised learning is worth further study. This paper proposes a reliable contrastive learning method for semi-supervised remote sensing image change detection. First, according to the task characteristics of change detection, we design the contrastive loss based on the changed areas to enhance the model's feature extraction ability for changed objects. Then, to improve the quality of pseudo labels in semi-supervised learning, we use the uncertainty of unlabeled data to select reliable pseudo labels for model training. Combining these methods, semi-supervised change detection models can make full use of unlabeled data. Extensive experiments on three widely used change detection datasets demonstrate the effectiveness of the proposed method. The results show that our semi-supervised approach has better performance than related methods. The code is available at https://github.com/VCISwang/RC-Change-Detection.