Publication
Title
Non-linear spectral unmixing by geodesic simplex volume maximization
Author
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.
Language
English
Source (journal)
IEEE journal of selected topics in signal processing. - New York, N.Y.
Publication
New York, N.Y. : 2011
ISSN
1932-4553 [print]
1941-0484 [online]
Volume/pages
5 :3 (2011) , p. 534-542
ISI
000290750700015
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 23.03.2011
Last edited 15.11.2022
To cite this reference