ST-Mamba: Spatio-Temporal Synergistic Model for Remote Sensing Change Detection

The advancement of remote sensing and deep learning has spurred interest in high-resolution image change detection (CD). However, pseudo-changes in multi-temporal images, due to complex scenes and variable imaging conditions, often lead to significant misdetection in current methods. To address this problem, we propose a new CD framework: Spatio-Temporal Mamba (ST-Mamba), which consists of three key components. Firstly, a Mamba-based Feature Extraction Module (MFEM) is designed as the encoder to extract essential features from multi-temporal images by leveraging Mamba’s capability to capture inherent information in long data sequences. Secondly, a Spatio-Temporal Synergy Module (STSM) is developed to unify the background features of multi-temporal feature maps into a common domain by employing the state-space model for spatio-temporal modeling. Finally, a Spatio-Temporal Fusion Module (STFM) is created to guide the fusion of image features at different scales and across channels by utilizing a feature map of the unified background features. Experimental results on five widely used change detection datasets show significant improvements over current state-of-the-art methods.