Collaborative Learning Network for Change Detection and Semantic Segmentation of Remote Sensing Images

Change detection of high-resolution remote sensing images is more attractive, since it can not only identify areas of changes but also identify types of changes. In this context, simultaneous change detection and semantic segmentation are natural and necessary. However, traditional methods put less emphasis on the cooperation of the above two tasks. In this letter, a novel method is proposed to realize the collaborative learning of change detection and semantic segmentation. By elaborately exploring the relevance and consistency between change detection and semantic segmentation, the proposed method synchronously enhanced feature separability of two tasks, and it outperformed a single change detection network or semantic segmentation network. Specifically, the proposed approach extracts multilevel bitemporal features by a backbone network, followed by two layer-by-layer decoders for learning change features and semantic features. On one hand, the interactive fusion module (IFM) fuses the changing features and semantic features together to increase the collaboration between the two tasks. On the other hand, the contrastive loss (CL) enhances the constraints between the two tasks. The advantages of the proposed method are demonstrated with respect to change region detection and change-type identification.