Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
This work addresses the problem of efficient and accurate classification of hyperspectral images for remote sensing applications, representing an incremental improvement over existing Mamba models.
The paper tackled hyperspectral image classification by proposing a multi-head spatial-spectral Mamba model that integrates spectral and spatial information, achieving 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.
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. The source code is available at \href{https://github.com/MHassaanButt/MHA\_SS\_Mamba}{GitHub}.