MambaCL: A Contrastive Learning Framework for Change Detection on Pseudotemporal Images Using Visual State Space Model

With the continuous development of remote sensing sensors and image processing technology, methods typified by deep learning are leading remote sensing change detection (RSCD) toward an increasingly refined direction. Due to the significant spatiotemporal heterogeneity in high-resolution remote sensing images (RSIs), current RSCD methods have attempted to enhance the robustness of models in dealing with easily confused backgrounds through data augmentation or attention mechanisms. However, these methods are often limited to modeling the differences in dual-temporal images and fail to address complex temporal perturbations, such as changes in imaging conditions and seasonal changes. To tackle this challenge, this article introduces a contrastive learning framework for change detection based on pseudotemporal images, utilizing the visual state space model (named MambaCL). Specifically, MambaCL proposes a three-branch pseudotemporal input model designed to explicitly model complex temporal change features. A FusionMamba encoder based on a visual state space model is constructed to efficiently extract and fuse long-term contextual features. Besides, a global dense joint contrast learning strategy is proposed to bolster the model’s robustness against temporal variations. Comparative experiments and ablation studies were conducted in this article on the BCDD, CLCD, and LEVIR-CD datasets. The quantitative results indicated that MambaCL performs effectively on the RSCD task, achieving state-of-the-art results on the above three benchmarks. Specifically, it obtained an F1-score of 89.84% on the BCDD dataset, an F1-score of 81.29% on the CLCD dataset, and an F1-score of 91.42% on the LEVIR-CD dataset, respectively.