# HS-Mamba: Full-Field Interaction Multi-Groups Mamba for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2504.15612 · HTML: https://mlsanity.com/paper/2504.15612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [RS3Mamba: Visual State Space Model for Remote Sensing Images Semantic Segmentation](https://mlsanity.com/paper/2404.02457/) (2024)
- [Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18401/) (2024)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12487/) (2024)
- [DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2406.07050/) (2024)
- [IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.05100/) (2024)
- [SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery](https://mlsanity.com/paper/1806.09807/) (2018)

## Abstract (arXiv)

Hyperspectral image (HSI) classification has been one of the hot topics in remote sensing fields. Recently, the Mamba architecture based on selective state-space models (S6) has demonstrated great advantages in long sequence modeling. However, the unique properties of hyperspectral data, such as high dimensionality and feature inlining, pose challenges to the application of Mamba to HSI classification. To compensate for these shortcomings, we propose an full-field interaction multi-groups Mamba framework (HS-Mamba), which adopts a strategy different from pixel-patch based or whole-image based, but combines the advantages of both. The patches cut from the whole image are sent to multi-groups Mamba, combined with positional information to perceive local inline features in the spatial and spectral domains, and the whole image is sent to a lightweight attention module to enhance the global feature representation ability. Specifically, HS-Mamba consists of a dual-channel spatial-spectral encoder (DCSS-encoder) module and a lightweight global inline attention (LGI-Att) branch. The DCSS-encoder module uses multiple groups of Mamba to decouple and model the local features of dual-channel sequences with non-overlapping patches. The LGI-Att branch uses a lightweight compressed and extended attention module to perceive the global features of the spatial and spectral domains of the unsegmented whole image. By fusing local and global features, high-precision classification of hyperspectral images is achieved. Extensive experiments demonstrate the superiority of the proposed HS-Mamba, outperforming state-of-the-art methods on four benchmark HSI datasets.