Multi-head spatial-spectral mamba for hyperspectral image classification

Spatial-Spectral Mamba (SSM) improves computational efficiency and captures long-range dependencies, addressing the limitations of transformers. However, traditional Mamba models often overlook the rich spectral information in hyperspectral images (HSIs) and struggle with high dimensionality and sequential data. To address these challenges, we propose the Spatial-Spectral Mamba with Multi-Head Self-Attention and Token Enhancement (MHSSMamba). This model integrates spatial and spectral information by enhancing spectral tokens and employing multi-head self-attention to capture complex relationships between spectral bands and spatial locations. It effectively manages long-range dependencies and the sequential nature of HSI data, preserving contextual information across spectral bands. MHSSMamba achieved classification accuracies of 98.56% on the Pavia University dataset, 99.00% on the University of Houston dataset and 98.54% on the Salinas dataset. The source code is available at https://github.com/mahmad000/MHSSMambaGitHub.