YonghaoXu / Ssun
Licence: mit
Spectral-Spatial Unified Networks for Hyperspectral Image Classification
Stars: ✭ 44
Programming Languages
python
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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
-
Install
Keras 2.2.4
from https://github.com/keras-team/keras withPython 3.6
.- Note: This repo is now updated with the
Tensorflow
backend engine. We have tested the code withTensorflow 1.13
. For theTheano
backend users, please refer to https://keras.io/#configuring-your-keras-backend for technical support.
- Note: This repo is now updated with the
-
Clone this repo.
git clone https://github.com/YonghaoXu/SSUN
Dataset
- Download the Pavia University image and the corresponding annotations.
Usage
- 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
Note
- 12/2019: Update the code with the
Tensorflow
backend engine.
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