Multi-head spatial-spectral mamba for hyperspectral image classification

Ahmad, adilMuhammad; Butt, Muhammad Hassaan Farooq; Usama, Muhammad; Altuwaijri, Hamad Ahmed; Mazzara, Manuel; Distefano, Salvatore; Khan, Adil Mehmood

doi:10.1080/2150704X.2025.2461330

Multi-head spatial-spectral mamba for hyperspectral image classification

Ahmad, adilMuhammad; Butt, Muhammad Hassaan Farooq; Usama, Muhammad; Altuwaijri, Hamad Ahmed; Mazzara, Manuel; Distefano, Salvatore; Khan, Adil Mehmood

Authors

adilMuhammad Ahmad

Muhammad Hassaan Farooq Butt

Muhammad Usama

Hamad Ahmed Altuwaijri

Manuel Mazzara

Salvatore Distefano

Professor Adil Khan A.M.Khan@hull.ac.uk
Professor

Abstract

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.

Citation

Ahmad, A., Butt, M. H. F., Usama, M., Altuwaijri, H. A., Mazzara, M., Distefano, S., & Khan, A. M. (2025). Multi-head spatial-spectral mamba for hyperspectral image classification. Remote Sensing Letters, 16(4), 15-29. https://doi.org/10.1080/2150704X.2025.2461330

Journal Article Type	Article
Acceptance Date	Jan 24, 2025
Online Publication Date	Feb 6, 2025
Publication Date	Jan 1, 2025
Deposit Date	Mar 17, 2025
Publicly Available Date	Jan 2, 2026
Journal	Remote Sensing Letters
Print ISSN	2150-704X
Electronic ISSN	2150-7058
Publisher	Taylor and Francis Group
Peer Reviewed	Peer Reviewed
Volume	16
Issue	4
Pages	15-29
DOI	https://doi.org/10.1080/2150704X.2025.2461330
Public URL	https://hull-repository.worktribe.com/output/5084308