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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

1. Please follow Tensorflow instruction to install all necessary packages and build it.
2. Please follow Keras instruction

• Clone this repository.
• Note: We currently only support Python 2.7

Traineval

datasets

1. download the datasets from here and save in 'data' folder in data_process' folder
2. normalized and get mirrore of original data

cd ./data_process
python expand.py

3. turn the data into small pieces open your matlab and run gen_train_all_bands.m in 'data_process' folder

4. 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.