Change Detection for Remote Sensing Images based on Semantic Prototypes and Contrastive Learning

Deep networks have achieved remarkable success in addressing the problem of change detection for bitemporal remote sensing images. While most networks focus on learning discriminating features using diverse backbone network architectures, few of them prioritize improving the performance of classification networks. In this paper, we propose a method called SPCL-BIT, which enhances the BIT method by replacing its Softmax classifier with a more discriminative classifier based on semantic prototypes and contrastive learning. In the proposed classifier, we introduce two semantic prototypes to represent the change class and the unchanged class. A semantic encoder is then employed to model the relationship between these two prototypes. To obtain effective prototype representations with good inter-class separability and intra-class cohesiveness, we adopt a contrastive loss. The learned classifier, named SPCL-CD, based on semantic prototypes and contrastive learning, can be integrated with the backbone networks of most existing change detection models to enhance their performance. Comprehensive experimental results demonstrate the effectiveness of the SPCL-BIT network, and the proposed classification network, SPCL-CD, shows varying degrees of improvement when combined with the backbones of state-of-the-art networks in change detection.