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MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification

Yapeng Li, Yong Luo, Lefei Zhang, Zengmao Wang*, Bo Du*

📝 Introduction

• To our best knowledge, the MambaHSI is the first image-level hyperspectral image classification model based on SSM, which can simultaneously model long-range interaction of whole image and integrate spatial and spectral image information.
• MambaHSI demonstrates the great potential of Mamba to be the next-generation backbone for hyperspectral image models.

🚀 Getting Started

Installation

conda create -n MambaHSI_env python=3.9
conda activate MambaHSI_env
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install packaging==24.0
pip install triton==2.2.0
pip install mamba-ssm==1.2.0
pip install spectral
pip install scikit-learn==1.4.1.post1
pip install calflops

Data Preparation

The dataset can download Google Drive and BaiduNetdisk.

data
└── UP/
    ├── PaviaU.mat 
    └── PaviaU_gt.mat
    ...
└── Houston/
    ├── Houston.mat 
    └── Houston_GT.mat
    ...
└── HanChuan/
    ├── WHU_Hi_HanChuan.mat 
    └── WHU_Hi_HanChuan_gt.mat
    ...
└── HongHu/  
    ├── WHU_Hi_HongHu.npy
    └── WHU_Hi_HongHu_gt.npy

Training:

python train_MambaHSI.py --dataset_index 0
python train_MambaHSI.py --dataset_index 1
python train_MambaHSI.py --dataset_index 2
python train_MambaHSI.py --dataset_index 3

🎖️ Main Results

Citation

If you find this project helpful for your research, please kindly consider citing our paper and give this repo ⭐️:

@ARTICLE{MambaHSI_TGRS24,
  author={Li, Yapeng and Luo, Yong and Zhang, Lefei and Wang, Zengmao and Du, Bo},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification}, 
  year={2024},
  volume={},
  number={},
  pages={1-16},
  keywords={Hyperspectral Image Classification;Mamba;State Space Models;Transformer},
  doi={10.1109/TGRS.2024.3430985}}

Acknowledgement

Part of our MambaHSI framework is referred to CVSSN and SSFCN. We thank all the contributors for open-sourcing.