@inproceedings{209cbcea0b4c4fd0b0bc257b9712f359,
title = "Band selection in RKHS for fast nonlinear unmixing of hyperspectral images",
abstract = "The profusion of spectral bands generated by the acquisition process of hyperspectral images generally leads to high computational costs. Such difficulties arise in particular with nonlinear unmixing methods, which are naturally more complex than linear ones. This complexity, associated with the high redundancy of information within the complete set of bands, make the search of band selection algorithms relevant. With this work, we propose a band selection strategy in reproducing kernel Hilbert spaces that allows to drastically reduce the processing time required by nonlinear unmixing techniques. Simulation results show a complexity reduction of two orders of magnitude without compromising unmixing performance.",
keywords = "band selection, Hyperspectral data, kernel methods, nonlinear unmixing",
author = "T. Imbiriba and Bermudez, \{J. C.M.\} and C. Richard and Tourneret, \{J. Y.\}",
note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/EUSIPCO.2015.7362664",
language = "English",
series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1651--1655",
booktitle = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
}