Code for the JSTARS2020 article 'Delving into Classifying Hyperspectral Images via Graphical Adversarial Learning'.

---

• Here I provide Tensorflow implementations for GAL, BGAC, and GAC.

• I was inspired to an extent by the two nice repositories
  https://github.com/KonstantinosF/Classification-of-Hyperspectral-Image
  and https://github.com/zhenxuan00/graphical-gan.
  I feel grateful to the authors for providing them.

* Environment & Main Dependencies

CentOS Linux release 7.2.1511 (Core)
Tesla K80 Graphic Processing Units
python 2.7.15
TensorFlow 1.14.0

* Usage

• Download hyperspectral data and add them to ./dataset.
  
• All the hyperparameters are in ./tflib/config.py.
  Set them to what you want when running a code.
  
• Run
  python GAL.py -GPU 0
  to see GAL in the local mode on the Salinas dataset.

* Citation

@article{Wang20GAL,
author={Wang, Guangxing and Ren, Peng},
journal={IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.},
title={Delving into Classifying Hyperspectral Images via Graphical Adversarial Learning},
volume={13},
number={},
pages={2019-2031},
month={},
year={2020}
}