FAPMNet: Flow-Aligned Prototype Memory Network for Semantic Change Detection in Remote Sensing Images

Semantic change detection (SCD) in bi-temporal remote sensing images aims to localize and semantically classify changed regions, which is crucial for land cover monitoring and disaster assessment. Existing methods often struggle with spatial misalignment and semantic inconsistency between temporal domains. In this letter, we propose FAPMNet, a Flow-Aligned Prototype Memory Network, which integrates a Mamba-based Vision State Space Model (VSSM) encoder for efficient long-range spatial modeling. To address temporal misalignment, a Flow Alignment Module (FAM) estimates dense flow fields to warp and align features. A Prototype Memory Module (PMM) further enhances semantic consistency via class-guided feature reconstruction. Evaluated on the SECOND and Landsat-SCD datasets, FAPMNet achieves new state-of-the-art results with 74.48% mIoU and 66.04%Fscdon SECOND, and 90.04% mIoU and 90.20%Fscdon Landsat-SCD, surpassing recent Transformer and Mamba-based baselines.