GMFIMamba: Remote sensing change detection based on group Mamba feature interaction

Abstract

With the advancement of satellite technology, high-resolution remote sensing images have been widely used in the field of change detection. Building Change Detection (BCD) and Building Damage Assessment (BDA) are both sub-tasks of change detection. BCD aims to detect structural changes in buildings over time, whereas BDA focuses on assessing the level of building damage after a disaster. BCD is of great value for urban planning, while BDA plays a crucial role in post-disaster rescue efforts. To address these tasks, we propose a change detection method based on Mamba, named GMFIMamba. Specifically, we design a Convolution–Visual State Space (Conv-VSS) block, which combines the local feature extraction capability of Convolutional Neural Networks (CNNs) with the global feature modeling ability of Mamba. By integrating local and global features, our approach improves the accuracy of change region detection. To tackle the issue of insufficient feature extraction for small-scale buildings in existing models, we introduce the Multi-branch Dilated Convolution Feature Enhancement Module (MCFEM). In addition, we design the Grouped Mamba-Based Bitemporal Features Interaction Module (GMBFIM) to facilitate effective interaction between bitemporal images, leading to more accurate change feature extraction. Experiments on three public datasets demonstrate that the proposed method achieves superior performance in both BCD and BDA tasks, proving its effectiveness.