3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection
This work addresses remote sensing change detection for environmental monitoring, presenting an incremental improvement over existing Mamba-based methods.
The paper tackles the problem of limited long-range dependency capture in Mamba-based remote sensing change detection by proposing a 3D selective scan module (3D-SSM) with spatiotemporal interaction and multi-branch feature extraction, achieving favorable performance on five benchmark datasets.
Existing Mamba-based approaches in remote sensing change detection have enhanced scanning models, yet remain limited by their inability to capture long-range dependencies between image channels effectively, which restricts their feature representation capabilities. To address this limitation, we propose a 3D selective scan module (3D-SSM) that captures global information from both the spatial plane and channel perspectives, enabling a more comprehensive understanding of the data.Based on the 3D-SSM, we present two key components: a spatiotemporal interaction module (SIM) and a multi-branch feature extraction module (MBFEM). The SIM facilitates bi-temporal feature integration by enabling interactions between global and local features across images from different time points, thereby enhancing the detection of subtle changes. Meanwhile, the MBFEM combines features from the frequency domain, spatial domain, and 3D-SSM to provide a rich representation of contextual information within the image. Our proposed method demonstrates favourable performance compared to state-of-the-art change detection methods on five benchmark datasets through extensive experiments. Code is available at https://github.com/VerdantMist/3D-SSM