MPS2L: Mutual Prediction Self-Supervised Learning for Remote Sensing Image Change Detection

In this article, we propose a novel mutual prediction self-supervised learning (MPS2L) method for remote sensing (RS) image change detection (CD). Compared with the previous self-supervised CD methods based on contrastive learning (CL), MPS2L employing a pixel-level training strategy based on masked image modeling (MIM) can effectively train the model to interpret the local scene of RS images. Utilizing global and local scenes and temporal change features extracted from masked bitemporal images to achieve cross-temporal mutual prediction makes the model have the ability to understand the overall observation scene and capture the change information. The training of the two abilities is carried out simultaneously, avoiding the problem of multiobjective conflict or mutual inhibition. To better focus on the changing regions in RS scenes, we further introduce a change feature interaction module (CFIM), comprising spatial and channel feature interaction. The channel interaction module (CIM) can facilitate the cross-temporal transmission of global scene information by channel attention, and the spatial interaction module (SIM) can promote the network to capture information on changing regions by spatial attention. The experimental results on three benchmark RS CD datasets demonstrate the effectiveness and priority of our proposed MPS2L compared to some existing state-of-the-art (SOTA) methods. The source code of the proposed MPS2L will be made available publicly at https://github.com/KustTeamWQW/MPS2L.