MGCD: Change Detection Focuses on Multiscale Style Domain Generalization

Change detection (CD) plays a crucial role in remote sensing (RS) applications. Although deep learning (DL)-based CD methods have achieved impressive performance, they typically rely on large amounts of well-annotated data, which is often scarce in real-world scenarios. This data scarcity leads to overfitting and limited generalization ability in conventional CD models. To address these challenges, this article proposes a novel few-sample CD framework named multiscale style DG method for change detection (MGCD). The core idea is to enhance the model’s ability to learn domain-invariant features by increasing data diversity across multiple style scales. Specifically, MGCD introduces global style diversity by incorporating out-of-domain natural images, and enriches local structural styles through unsupervised clustering and randomization. Additionally, a semantic consistency supervision (SCS) strategy is designed to guide multitemporal feature learning, enabling the network to better capture changes across diverse styles and scales. Extensive experiments conducted on three benchmark datasets demonstrate the effectiveness and robustness of the proposed MGCD framework in few-sample CD tasks.