SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models

Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia

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The code in this toolbox implements the "SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models".

The codes for this research includes two parts, spectral-spatial diffusion module and attention-based classification module. This repository is for the attention-based classification module.

More specifically, it is detailed as follow.

Citation

Please kindly cite the papers if this code is useful and helpful for your research.

N. Chen, J. Yue, L. Fang and S. Xia, "SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3310023.

@ARTICLE{10234379,
  author={Chen, Ning and Yue, Jun and Fang, Leyuan and Xia, Shaobo},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models}, 
  year={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TGRS.2023.3310023}}

How to use it?

1. Prepare raw data and diffusion features
   • Raw data is origin HSI data, likes IP, PU, SA datasets, You need to separate the training and test sets in advance. Or you can download ours from baiduyun, extract codes is pabk.
   • The classification module requires providing the features extracted by the diffusion module as input. We provide the diffusion features extracted in our experiments for researchers to reproduce the results. For the convenience of testing, we have provided all diffusion features data before PCA. Please download the specific data from baiduyun, extract codes is pabk, you can also get data from google_drive.
   • To train a diffusion model by yourself, you can use this code repository spectral-spatial diffusion module.
2. Modify the path of diffusion features in the params to ensure that the code can read them.
3. Run the code

   python workflow.py
   

Others

If you want to run the code in your own data, you can accordingly change the input (e.g., data, labels) and tune the parameters.

If you encounter the bugs while using this code, please do not hesitate to contact us.

Licensing

Copyright (C) 2023 Ning Chen

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.