Publication
Title
Hyperspectral unmixing with endmember variability via alternating angle minimization
Author
Abstract
In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class. This inherent spectral variability can pose severe difficulties in classical unmixing approaches. In this paper, we present a new algorithm for dealing with endmember variability in spectral unmixing, based on the geometrical interpretation of the resulting unmixing problem, and an alternating optimization approach. This alternating-angle-minimization algorithm uses sets of spectra to represent the variability present in each class and attempts to identify the subset of endmembers which produce the smallest reconstruction error. The algorithm is analogous to the popular multiple endmember spectral mixture analysis technique but has a much more favorable computational complexity while producing similar results. We illustrate the algorithm on several artificial and real data sets and compare with several other recent techniques for dealing with endmember variability.
Language
English
Source (journal)
IEEE transactions on geoscience and remote sensing / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York
Publication
New York : 2016
ISSN
0196-2892
Volume/pages
54:8(2016), p. 4983-4993
ISI
000381434600050
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
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Research group
Publication type
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Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 07.07.2016
Last edited 09.06.2017
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