  Spatial-Spectral Mamba (SSM) improves computational efficiency and captures
long-range dependencies, addressing Transformer limitations. However,
traditional Mamba models overlook rich spectral information in HSIs and
struggle with high dimensionality and sequential data. To address these issues,
we propose the SSM with multi-head self-attention and token enhancement
(MHSSMamba). This model integrates spectral and spatial information by
enhancing spectral tokens and using multi-head attention to capture complex
relationships between spectral bands and spatial locations. It also manages
long-range dependencies and the sequential nature of HSI data, preserving
contextual information across spectral bands. MHSSMamba achieved remarkable
classification accuracies of 97.62\% on Pavia University, 96.92\% on the
University of Houston, 96.85\% on Salinas, and 99.49\% on Wuhan-longKou
datasets.
