@inproceedings{818f58de91cf4776bc393a4d32404386,
title = "Smooth and sparse hyperspectral unmixing using an l0 penalty",
abstract = "Hyperspectral unmixing is an important technique for analyzing hyperspectral remote sensing images. We propose an estimation algorithm that, simultaneously, encourages smoothness in the endmembers and sparseness in the abundances by using first order roughness and l0 penalties. The method is evaluated both on simulated data and a real hyperspectral image of an urban landscape.",
keywords = "Blind signal separation, Cyclic descent, L penalty, Linear unmixing, Roughness penalty",
author = "Jakob Sigurdsson and Ulfarsson, \{Magnus O.\} and Sveinsson, \{Johannes R.\}",
note = "Funding Information: This work was supported by the Research Fund of the University of Iceland and The Icelandic Reasearch Fund (130635-051) Publisher Copyright: {\textcopyright} 2013 IEEE.; 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013 ; Conference date: 26-06-2013 Through 28-06-2013",
year = "2013",
month = jun,
day = "28",
doi = "10.1109/WHISPERS.2013.8080637",
language = "English",
isbn = "9781509011193",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)",
address = "United States",
}