SM-HAD: Spectrum Mamba for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) is vital in remote sensing and surveillance, where the goal is to identify rare or unknown targets without prior labels. However, HAD remains challenging due to: (1) high spectral dimensionality, (2) complex spectral–spatial dependencies, (3) diverse anomaly scales, and (4) the need for computationally efficient inference. To address these challenges, we propose SM-HAD, a unified encoder–decoder framework that integrates three complementary modules. First, the Ortho Spectrum Fourier Block (OSFB) mitigates spectral redundancy by projecting features into the frequency domain and selectively enhancing informative spectral modes. Second, the Masked Vanilla Attention Block (MVAB) explicitly preserves local spatial relations through a context-aware masking mechanism, improving fine-grained anomaly localization. Third, the Residual Mamba Block (RMB) leverages state-space modeling to capture long-range spatial dependencies with linear complexity, enabling scalable inference. Extensive experiments and ablation studies on six benchmark datasets demonstrate that SM-HAD consistently outperforms 18 State-of-the-Art (SoTA) methods, achieving competitive accuracy while requiring fewer parameters and lower FLOPs. These properties make SM-HAD highly suitable for real-time and resource-constrained applications. The source code is available at https://github.com/Tanishq251/SM-HAD.