Title
Hyperspectral unmixing with endmember variability via alternating angle minimization Hyperspectral unmixing with endmember variability via alternating angle minimization
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
New York ,
Subject
Economics
Physics
Chemistry
Engineering sciences. Technology
Source (journal)
IEEE transactions on geoscience and remote sensing / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York
Volume/pages
54(2016) :8 , p. 4983-4993
ISSN
0196-2892
ISI
000381434600050
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
Full text (open access)
https://repository.uantwerpen.be/docman/irua/70f638/134280.pdf
Handle