# Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification

Source: [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa76e81e209131cea6fd94>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:46:56.735Z

Authors: Muhammad Ahmad, Muhammad Hassaan Farooq Butt, Muhammad Usama, Hamad Ahmed Altuwaijri, Manuel Mazzara, Salvatore Distefano

Published: 2024-08-02T12:27:15.000Z

## Abstract

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}.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.01224>)

[PDF](<https://arxiv.org/pdf/2408.01224.pdf>)