High-Resolution Remote Sensing Image Change Detection Based on Efficient-Mamba Feature Interaction and Multiscale Refinement

Change detection in high-resolution (HR) remote sensing images has emerged as a fundamental task in Earth observation, aiming to accurately identify and delineate land surface changes over time. Nevertheless, its performance is often constrained by pseudo-changes arising from style discrepancies between bi-temporal images, as well as those introduced by local shadows. Recently, Mamba-based architectures have attracted significant interest due to their efficient modeling capabilities. Building upon this foundation, this paper proposes an Efficient-Mamba Feature Interaction and Multiscale Refinement (EFMR) method for HR remote sensing image change detection, which enhances overall detection accuracy while effectively mitigating pseudo-changes. Firstly, EFMR employs the Efficient Mamba Feature Interaction Module (EMIM) for efficient spatiotemporal alignment, emphasizing structural differences and suppressing pseudo-changes caused by style discrepancies. To mitigate shadow-induced pseudo-changes, EFMR utilizes the Multiscale Feature Refinement Module (MFRM) to refine multi-level change features through contextual modeling and residual enhancement. Additionally, to recover spatial details lost during upsampling, EFMR integrates a decoder based on the Enhanced Vision State Space Block (EVSSB), combining 2D state-space modeling with multi-kernel depth-wise convolutions. On three public datasets, qualitative and quantitative experiments show that the proposed method achieves superior performance over nine state-of-the-art techniques in both overall accuracy and detailed visualization.