Domain Knowledge-Guided Self-Supervised Change Detection for Remote Sensing Images

As one of the most popular topics in the field of Earth observation using remote sensing images, change detection (CD) provides great practical and valuable significance for many fields. Although the majority of supervised methods have made great progress by introducing deep learning in the CD field, they are still limited by manually labeled data. In comparison, unsupervised methods do not require manually labeled data, but the accuracy of CD is difficult to be improved due to the lack of constraints or guidance during training. To tackle these issues, we propose a novel domain knowledge-guided self-supervised learning approach for unsupervised CD by fusing the domain knowledge of remote sensing indices during training and inference. Furthermore, we calculate cosine similarity to select the high-similarity feature vectors outputted by the mean teacher and student networks to implement the hard negative sampling strategy, which effectively improves the CD performance. Compared with other supervised and unsupervised CD methods, our proposed approach achieves state-of-the-art performance with a Kap of 53.34% and an F1 of 55.69% on the Onera Satellite Change Detection dataset. Fusing domain knowledge to guide model training and inference obtains an improvement of 5.83% in Kap and 5.13% in F1, which further narrows the performance gap between unsupervised and supervised CD.