@inproceedings{653ee48090bc480c939ab0b17f6e9290,
title = "Sparse pixel-wise spectral unmixing - Which algorithm to use and how to improve the results",
abstract = "Recently, many sparse approximation methods have been applied to solve spectral unmixing problems. These methods in contrast to traditional methods for spectral unmixing are designed to work with large a-prori given spectral dictionaries containing hundreds of labelled material spectra enabling to skip the expensive endmember extraction and labelling step. However, it has been shown that sparse approximation methods sometimes have problems with selection of correct spectra from the dictionary when these are similar. In this paper we study the detection and approximation accuracy of different sparse approximation methods as well as the influence of the proposed modifications.",
keywords = "Hyperspectral image, sparse unmixing, spectral dictionary",
author = "Jakub Bieniarz and Rupert Muller and Zhu, {Xiao Xiang} and Peter Reinartz",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/IGARSS.2015.7326411",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2860--2863",
booktitle = "2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings",
}