Multicue Contrastive Self-Supervised Learning for Change Detection in Remote Sensing

Contrastive self-supervised learning (CSSL) is a promising method in extracting effective features from unlabeled data. It performs well in image-level tasks, such as image classification and retrieval. However, the existing CSSL methods are not suitable for pixel-level tasks, e.g., change detection (CD), since they ignore the correlation between local patches or pixels. In this paper, we firstly propose a multi-cue contrastive self-supervised learning (MC-CSSL) method to derive dense features for change detection. Besides data augmentation, the MC-CSSL takes advantage of more cues based on the semantic meaning and temporal correlation of local patches. Specially, the positive pair is built from local patches with the similar semantic meaning or temporal ones with the same geographic location. The assumption is that local patches belonging to the same kind of land-covering tend to share similar features. Secondly, the affinity matrix is truncated and introduced to extract change information between two temporal patches obtained from different types of sensors. As a result, some initial unchanged pixels are selected to serve as the supervision for mapping the dense features into a consistent space. Based on the distance between all bi-temporal pixels in the consistent space, a difference image (DI) is generated and more unchanged pixels can be available. The dense feature mapping and unchanged pixel updating proceed alternately. The proposed CD method is evaluated in both homogeneous and heterogeneous cases and the experimental results demonstrate its effectiveness and priority after comparison with some existing state-of-the-art methods. The source code will be available at https://github.com/Yang202308/ChangeDetection_CSSL.