Keras implementation of our method for hyperspectral image classification.

Spectral–Spatial Unified Networks for Hyperspectral Image Classification

Please cite our papers if you find it useful for your research.

@article{ssun,
  author={Y. Xu and L. Zhang and B. Du and F. Zhang},
  journal={IEEE Trans. Geos. Remote Sens.},
  title={Spectral-Spatial Unified Networks for Hyperspectral Image Classification},
  year={2018},
  volume={56},
  number={10},
  pages={5893-5909},
  ISSN={0196-2892},
  month={Oct}
}

@inproceedings{bglstm,
  title={A Band Grouping Based LSTM Algorithm for Hyperspectral Image Classification},
  author={Y. Xu and B. Du and L. Zhang and F. Zhang},
  booktitle={CCF Chinese Conference on Computer Vision},
  pages={421--432},
  year={2017},
  organization={Springer}
}

• Install Keras 2.2.4 from https://github.com/keras-team/keras with Python 3.6.

  • Note: This repo is now updated with the Tensorflow backend engine. We have tested the code with Tensorflow 1.13. For the Theano backend users, please refer to https://keras.io/#configuring-your-keras-backend for technical support.

• Clone this repo.

git clone https://github.com/YonghaoXu/SSUN

• Download the Pavia University image and the corresponding annotations.

• Replace the file path for the hyperspectral data in HyperFunctions.py with yours.
• Run SSUN.py.
• Change the s1s2 index in SSUN.py to switch from different grouping strategies.
  • Left: Strategy 1 s1s2 = 1
  • Right: Strategy 2 s1s2 = 2

• 12/2019: Update the code with the Tensorflow backend engine.