RHDN for Hyperspectral Fusion

The python code implementation of the paper "A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network" (IEEE Transactions on Neural Networks and Learning Systems 2022)

Requirements

• Ubuntu 20.04 cuda 11.0
• Python 3.8 Pytorch 1.7

Usage

Brief description

• data floder stores training and testing dataset.
• fusion floder stores the fused data of network test.
• weights floder stores optimal network training parameters.
• Attention.py provides SpatialAttention and ChannelAttention modules.
• dataloader.py generates data iterator.
• Model.py defines the Residual Hyper-Dense Network(RHDN).
• Model_train.py uses Train and Test flags to control model training and testing.
• More details are commented in the code.

Sample Test

1. The Test and Train flags set to True and False in Model_train.py.
2. Run Model_train.py to load the net_weihts.pth to obtain the fused data.

explain

• Due to the limitation of github upload capacity, we only upload five sample images of Pavia.
• Note that you can download all the test and fused images of Pavia from Baidu Cloud links:https://pan.baidu.com/s/1ytquzgD_Jvwa2czJPjElXQ(Access Code:wyw2)

Citation

@ARTICLE{9831112,
  author={Qu, Jiahui and Xu, Zhangchun and Dong, Wenqian and Xiao, Song and Li, Yunsong and Du, Qian},
 journal={IEEE Transactions on Neural Networks and Learning Systems},
 title={A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network},
 year={2022},
 volume={},
 number={},
 pages={1-15},
 doi={10.1109/TNNLS.2022.3189049}}