MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification
September 24, 2024 ยท View on GitHub
๐ 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.
๐ 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
โโโ UP/
โโโ PaviaU.mat
โโโ PaviaU_gt.mat
...
โโโ Houston/
โโโ Houston.mat
โโโ Houston_GT.mat
...
โโโ HanChuan/
โโโ WHU_Hi_HanChuan.mat
โโโ WHU_Hi_HanChuan_gt.mat
...
โโโ HongHu/
โโโ WHU_Hi_HongHu.npy
โโโ 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
๐๏ธ Main Results
Pavia University Results
Houston Results
HanChuan Results
HongHu Results
Citation
If you find this project helpful for your research, please kindly consider citing our paper and give this repo โญ๏ธ:
@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.