Publication
Title
Non-linear fully-constrained spectral unmixing
Author
Abstract
In hyperspectral unmixing, one often observes that the interactions between the endmember spectra can contain strong non-linear effects. Recently, a new endmember extraction algorithm has been proposed that is capable of dealing with a non-linearly shaped data manifold, based upon a combination of geodesic distances and a volume-maximizing search algorithm. Once the endmembers have been found, the pixels have to be decomposed into their abundances, which within the linear mixing assumption becomes a constrained least-squares problem. These techniques are however not fit for dealing with non-linearly mixed data. In this work, we present an algorithm that is capable of unmixing non-linearly mixed data, and which obeys the positivity and sum-to-one constraint usually imposed on the abundance vectors. The algorithm is based upon a reformulation of the recently developed SPU algorithm in terms of distance geometry. A demonstration of the algorithm on the Cuprite data set is provided.
Language
English
Source (book)
IEEE IGARSS2011, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, Canada, July 24-29, 2011
Publication
S.l. : 2011
ISBN
978-1-4577-1003-2
Volume/pages
p. 1295-1298
ISI
000297496301089
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
Identification
Creation 03.11.2011
Last edited 08.06.2017
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