Publication
Title
Fully constrained least squares spectral unmixing by simplex projection
Author
Abstract
We present a new algorithm for linear spectral mixture analysis, which is capable of supervised unmixing of hyperspectral data while respecting the constraints on the abundance coefficients. This simplex-projection unmixing algorithm is based upon the equivalence of the fully constrained least squares problem and the problem of projecting a point onto a simplex. We introduce several geometrical properties of high-dimensional simplices and combine them to yield a recursive algorithm for solving the simplex-projection problem. A concrete implementation of the algorithm for large data sets is provided, and the algorithm is benchmarked against well-known fully constrained least squares unmixing (FCLSU) techniques, on both artificial data sets and real hyperspectral data collected over the Cuprite mining region. Unlike previous algorithms for FCLSU, the presented algorithm possesses no optimization steps and is completely analytical, severely reducing the required processing power.
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 : 2011
ISSN
0196-2892
Volume/pages
49:11(2011), p. 4112-4122
ISI
000297280100002
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 15.07.2011
Last edited 15.07.2017
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