Mamba-FCS: Joint Spatio-Frequency Feature Fusion, Change-Guided Attention, and SeK Inspired Loss for Enhanced Semantic Change Detection in Remote Sensing

Semantic Change Detection (SCD) in remote sensing imagery requires models that integrate extensive spatial context for broad geographic patterns, computational efficiency for large-scale datasets, and sensitivity to class-imbalanced land-cover transitions to detect rare or asymmetric changes. Early SCD approaches relied on Convolutional Neural Networks, which excel in local feature extraction but falter in modeling global spatial context due to limited receptive fields. Transformers mitigate this by capturing long-range dependencies via self-attention, yet their quadratic complexity impairs efficiency on vast remote sensing data. Emerging Mamba architectures, based on state-space models, strike a balance with linear complexity and robust long-range modeling, delivering efficient global context capture and improved performance. In this study, we introduce Mamba-FCS, an SCD framework leveraging a Visual State Space Model backbone, with three key contributions: A Joint Spatio-Frequency Fusion block that integrates log-amplitude frequency-domain features to sharpen edges and mitigate illumination artifacts, a Change-Guided Attention (CGA) module that explicitly bridges the intertwined Binary Change Detection and SCD tasks, and a novel loss function inspired by the Separated Kappa (SeK) metric to optimize for class imbalance. Experiments on the benchmark datasets show that Mamba-FCS consistently outperforms recent state-of-the-art algorithms. Ablation studies indicate that spatio–frequency fusion and CGA mainly sharpen boundaries and suppress hallucinated changes, while the SeK-inspired loss improves minority-class semantics. These results highlight the potential of Mamba-FCS as a scalable and accurate approach for remote sensing change detection.