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 1.1.0
from https://github.com/keras-team/keras withPython 2.7
.- Note: This repo is trained with the
Theano 0.8.2
backend. For theTensorflow
backend users, please refer to https://keras.io/#configuring-your-keras-backend for technical support.
- Note: This repo is trained with the
-
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 inSSUN.py
to switch from different grouping strategies.- Left: Strategy 1
s1s2 = 1
- Right: Strategy 2
s1s2 = 2
- Left: Strategy 1