Sen2CHRIS dataset for training and test

Compact High Resolution Imaging Spectrometer, namely CHRIS, is carried on PRoject for On Board Autonomy 1 (PROBA-1) satellite and provides hyperspectral images.However, CHRIS images are always accompanied by hybrid noise, stripe gap, and high cloud cover. Furthermore, all satellite passes are systematically acquired according to a fixed acquisition plan. Observation over a new specific area should be performed by submitting the request to add a new site to the acquisition plan, which costs much money and time.

To verify the model performance of solving generalized spectral super-resolution, Sen2CHRIS data set is generated from three freely available data subsets, including Xiong'an, Washington DC Mall, and Chikusei, by downsampling the spectral channels of free hyperspectral data to the same of CHRIS and Sentinel-2 using Hysure.

The file named "Chikusei_CHRIS.h5" contains 1016 training samples where the high-resolution multispectral images are with size of 128×128×4 while the size of low-resolution multispectral images is 64×64×4. Moreover, we also give some test images in the "Chikusei_test.mat".

The file named "DCMall_CHRIS.h5" contains 2424 training samples where the high-resolution multispectral images are with size of 64×64×4 while the size of low-resolution multispectral images is 32×32×4. Moreover, we also give some test images in the "DCMall_test.mat".

The file named "Xiongan_CHRIS.h5" contains 1216 training samples where the high-resolution multispectral images are with size of 128×128×4 while the size of low-resolution multispectral images is 64×64×4. Moreover, we also give some test images in the "Xiongan_test.mat". Details about generalized spectral super-resolution can be found in the following paper.

Paper: J. He, Q. Yuan, J. Li, and L. Zhang, "PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images," Information Fusion, vol. 80, pp. 205–225, 2022.

More information about the author can be found at https://jianghe96.github.io/

If this dataset is helpful please cite as:

@article{He2022PoNet,
title={PoNet: A universal physical optimization-based spectral super-resolution network for arbitrary multispectral images},
author={He, Jiang and Yuan, Qiangqiang and Li, Jie and Zhang, Liangpei},
journal={Information Fusion},
volume={80},
pages={205--225},
year={2022},
}