3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection

TL;DR

This paper introduces a 3D selective scan module (3D-SSM) that captures global spatial and channel information to improve remote sensing change detection, demonstrating superior performance on multiple datasets.

Contribution

The paper proposes the 3D-SSM and its components, SIM and MBFEM, to enhance feature representation and change detection accuracy in remote sensing images.

Findings

Outperforms state-of-the-art methods on five benchmark datasets

Effectively captures long-range dependencies and global information

Enhances subtle change detection through bi-temporal feature interaction

Abstract

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