# Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2502.06427 · HTML: https://mlsanity.com/paper/2502.06427/

## Key facts

- Cited by 1 papers in the map (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 14 of its 55 references that are in the map.
- First posted on arXiv 10 Feb 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, 14 of 14)

- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [UIU-Net: U-Net in U-Net for Infrared Small Object Detection](https://mlsanity.com/paper/2212.00968/) (2022)
- [A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective](https://mlsanity.com/paper/2209.13232/) (2022)
- [Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects](https://mlsanity.com/paper/2101.06116/) (2021)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Mamba-in-Mamba: Centralized Mamba-Cross-Scan in Tokenized Mamba Model for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12003/) (2024)
- [DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2406.07050/) (2024)
- [DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection](https://mlsanity.com/paper/2404.00694/) (2024)
- [Pyramid Hierarchical Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.14945/) (2024)
- [WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01231/) (2024)
- [Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2408.01224/) (2024)
- [Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2305.16682/) (2023)

## Built on by (most cited first, 1 of 1)

- [Token Clustering and Semantic Sequence Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2609.28580/) (2026)

## Abstract (arXiv)

Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, including conventional machine learning and convolutional neural networks (CNNs), often struggle to effectively capture these intricate spectral-spatial features and global contextual information. Transformer-based models, while powerful in capturing long-range dependencies, often demand substantial computational resources, posing challenges in scenarios where labeled datasets are limited, as is commonly seen in HSI applications. To overcome these challenges, this work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms. The model introduces a novel hybridization of state-space modeling and Gated Recurrent Units (GRU), capturing both linear and nonlinear spatial-spectral dynamics. GraphMamba enhances the ability to model complex spatial-spectral relationships while maintaining scalability and computational efficiency across diverse HSI datasets. Through comprehensive experiments, we demonstrate that GraphMamba outperforms existing state-of-the-art models, offering a scalable and robust solution for complex HSI classification tasks.