An Remote Sensing Datasets from AVIRIS Data Portal
The data is sourced from AVIRIS Data Portal. We collected the HSI data between 2008 and 2012, and the original HSI data was partitioned into 256 × 256 sub-images, resulting in 1,735 HSI sub-images. We then applied k-means clustering to categorize the HSI sub-images into five types: City, Mountain, Forest, Farm, and Other. Any misclassified sub-images were manually corrected. From each class, we selected 200 HSI sub-images, forming a dataset of 1,200 sub-images in total. The dataset was then split into training, validation, and testing sets, consisting of 1,000, 100, and 100 HSI sub-images, respectively.
| Dataset | Link | Size |
|---|---|---|
| Training Dataset | [download link] | 18.8GB, 1000 examples |
| Validation Dataset | [download link] | 100 examples |
| Testing Dataset | [download link] | 100 examples |
Note: the data in all datasets are stored in .npy formats.
@ARTICLE{10474407,
author={Hsu, Chih-Chung and Jian, Chih-Yu and Tu, Eng-Shen and Lee, Chia-Ming and Chen, Guan-Lin},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat},
year={2024},
volume={62},
number={},
pages={1-16},
keywords={Hyperspectral imaging;Real-time systems;Sensors;Decoding;Compressed sensing;Training;Task analysis;Compressed sensing;deep learning (DL);hyperspectral image (HSI);hyperspectral restoration;real-time applications},
doi={10.1109/TGRS.2024.3378828}}and
@misc{lee2024prompthsiuniversalhyperspectralimage,
title={PromptHSI: Universal Hyperspectral Image Restoration Framework for Composite Degradation},
author={Chia-Ming Lee and Ching-Heng Cheng and Yu-Fan Lin and Yi-Ching Cheng and Wo-Ting Liao and Chih-Chung Hsu and Fu-En Yang and Yu-Chiang Frank Wang},
year={2024},
eprint={2411.15922},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2411.15922},
}