This is an implementation of Hyperspectral Image Super-Resolution Using Spectrum and Feature Context.
Three public datasets, i.e., CAVE, Harvard, Foster, are employed to verify the effectiveness of the proposed MCNet. Since there are too few images in these datasets for deep learning algorithm, we augment the training data. With respect to the specific details, please see the implementation details section.
Moreover, we also provide the code about data pre-processing in folder data pre-processing. The folder contains three parts, including training set augment, test set pre-processing, and band mean for all training set.
python 2.7, Pytorch 0.3.1, cuda 9.0
The ADAM optimizer with beta_1 = 0.9, beta _2 = 0.999 is employed to train our network. The learning rate is initialized as 10^-4 for all layers, which decreases by a half at every 35 epochs.
You can train or test directly from the command line as such:
To qualitatively measure the proposed MCNet, three evaluation methods are employed to verify the effectiveness of the algorithm, including peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and spectral angle mapping (SAM).
| Scale | CAVE | Harvard | Foster |
|---|---|---|---|
| x2 | 45.300 / 0.9739 / 2.217 | 46.342 / 0.9830 / 1.880 | 58.859 / 0.9988 / 4.052 |
| x3 | 41.198 / 0.9524 / 2.794 | 42.778 / 0.9632 / 2.203 | 54.575 / 0.9967 / 5.046 |
| x4 | 39.192 / 0.9321 / 3.221 | 40.077 / 0.9373 / 2.407 | 52.215 / 0.9939 / 5.618 |
Please consider cite this paper if you find it helpful.
@article{wang2020hyper,
title={Hyperspectral Image Super-Resolution Using Spectrum and Feature Context},
author={Q. Wang, Q. Li and X. Li},
journal={IEEE Transactions on Industrial Electronics},
year={2020},
doi={10.1109/TIE.2020.303809}
}
If you has any questions, please send e-mail to [email protected].