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Advisor(s)
Abstract(s)
In hyperspectral imagery a pixel typically consists mixture of spectral signatures of reference substances, also called endmembers. Linear spectral mixture analysis, or linear unmixing, aims at estimating the number of endmembers, their spectral signatures, and their abundance fractions.
This paper proposes a framework for hyperpsectral unmixing. A blind method (SISAL) is used for the estimation of the unknown endmember signature and their abundance fractions. This method solve a non-convex problem by a sequence of augmented Lagrangian optimizations, where the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The proposed framework simultaneously estimates the number of endmembers present in the hyperspectral image by an algorithm based on the minimum description length (MDL) principle. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of the proposed algorithm.
Description
Keywords
Blind hyperspectral unmixing Minimum volume simplex Minimum description length MDL Variable splitting augmented lagrangian Dimensionality reduction
Citation
NASCIMENTO, José M. P.; BIOUCAS-DIAS, José M. - Hyperspectral imagery framework for unmixing and dimensionality estimation. Pattern Recognition - Applications and Methods. ISBN 978-3-642-36529-4. Vol. 204. 193-204, 2013
Publisher
Springer-Verlag