@inproceedings{dd7af425f1c14a6e8ce1d791fca42db6,
title = "A dataset with ground-truth for hyperspectral unmixing",
abstract = "Spectral unmixing is one of the most important issues of hyperspectral data processing. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms. In this work, we create several experimental scenes in our laboratory with controlled settings where the pure material spectra and material compositions are known. Lab-made hyperspectral datasets with these scenes are then provided, and mutually validated with typical linear and nonlinear unmixing algorithms.",
keywords = "Ground-truth, Hyperspectral imaging, Spectral unmixing, Unmixing database",
author = "Min Zhao and Jie Chen",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8518410",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5077--5080",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
}