All Projects → MeiShaohui → Hyperspectral Image Spatial Super Resolution Via 3d Full Convolutional Neural Network
MeiShaohui / Hyperspectral Image Spatial Super Resolution Via 3d Full Convolutional Neural Network
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Hyperspectral Image Spatial Super-Resolution via 3D-Full-Convolutional-Neural-Network
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Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network
By Shaohui Mei, Xin Yuan, Jingyu Ji , Yifan Zhang, Shuai Wan, Qian Du
Introduction
The 3D-FRCNN is an unified framework for hyperspectral image super-resolutiom(SR) with a single network. You can use the code to train/evaluate a network for hsi super-resolution(SR). For more details, please refer to our paper.
Proposed Framework
Some SR results
Citing our work
@Article{rs9111139,
AUTHOR = {Mei, Shaohui and Yuan, Xin and Ji, Jingyu and Zhang, Yifan and Wan, Shuai and Du, Qian},
TITLE = {Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network},
JOURNAL = {Remote Sensing},
VOLUME = {9},
YEAR = {2017},
NUMBER = {11},
ARTICLE NUMBER = {1139},
URL = {http://www.mdpi.com/2072-4292/9/11/1139},
ISSN = {2072-4292},
DOI = {10.3390/rs9111139}
}
Installation
Install Keras
- Please follow Tensorflow instruction to install all necessary packages and build it.
- Please follow Keras instruction
- Clone this repository.
- Note: We currently only support Python 2.7
Traineval
datasets
- download the datasets from here and save in 'data' folder in data_process' folder
- normalized and get mirrore of original data
cd ./data_process
python expand.py
-
turn the data into small pieces open your matlab and run gen_train_all_bands.m in 'data_process' folder
-
prepare the train data for keras
cd ./data_process
python get_to_train.py
training
python train_network.py
Test
We release one pretrained models: model_pa.h5 in "model" folder for Pavia dataset. Do testing
python predict.py
will show both reconstructed images and PSNR/SSIM/SAM.
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