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[IEEE TGRS 2018] Spectral-Spatial Unified Networks for Hyperspectral Image Classification

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Spectral-Spatial Unified Networks for Hyperspectral Image Classification

Keras implementation of our method for hyperspectral image classification.

Paper

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}
}

Installation

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

Dataset

Usage

  • 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

Note

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

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