Bi-Temporal Remote Sensing Change Detection with State Space Models

Change detection in very high-resolution (VHR) remote sensing images has gained significant attention, particularly with the rise of deep learning techniques like CNNs and Transformers. The Mamba structure, successful in computer vision, has been applied to this domain, enhancing computational efficiency. However, much of the research focuses on improving global modeling, neglecting the role of local information crucial for change detection. Moreover, there remains a gap in understanding which structural modifications are more suited for the change detection task. This paper investigates the impact of different scanning mechanisms within Mamba, evaluating five mainstream methods to optimize its performance in change detection. We propose LBCDMamba, a novel architecture based on our proposed LocalGlobal Selective Scan Module, which effectively integrates global and local information through a unified scanning strategy. To address the lack of fine-grained details in current models, we propose a Multi-Branch Patch Attention module, which captures both local and global features by partitioning data into smaller patches. Additionally, a Bi-Temporal Feature Fusion module is proposed to fuse bi-temporal features, improving temporalspatial feature representation. Extensive experiments on three benchmark datasets demonstrate the superior performance of LBCDMamba outperforms existing popular methods in change detection tasks. This work also provides new insights into optimizing Mamba for change detection, with potential applications across remote sensing and related fields.