Title
Non-linear spectral unmixing by geodesic simplex volume maximizationNon-linear spectral unmixing by geodesic simplex volume maximization
Author
Faculty/Department
Faculty of Sciences. Physics
Research group
Vision lab
Publication type
article
Publication
New York, N.Y.,
Subject
Physics
Source (journal)
IEEE journal of selected topics in signal processing. - New York, N.Y.
Volume/pages
5(2011):3, p. 534-542
ISSN
1932-4553
1941-0484
ISI
000290750700015
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Spectral mixtures observed in hyperspectral imagery often display non-linear mixing effects. Since most traditional unmixing techniques are based upon the linear mixing model, they perform poorly in finding the correct endmembers and their abundances in the case of non-linear spectral mixing. In this paper, we present an unmixing algorithm that is capable of extracting endmembers and determining their abundances in hyperspectral imagery under non-linear mixing assumptions. The algorithm is based upon simplex volume maximization, and uses shortest-path distances in a nearest-neighbor graph in spectral space, hereby respecting the non-trivial geometry of the data manifold in the case of non-linearly mixed pixels.We demonstrate the algorithm on an artificial data set, the AVIRIS Cuprite data set, and a hyperspectral image of a heathland area in Belgium.
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