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Advisor(s)
Abstract(s)
This paper is an elaboration of the simplex identification via split augmented Lagrangian (SISAL) algorithm
(Bioucas-Dias, 2009) to blindly unmix hyperspectral data. SISAL is a linear hyperspectral unmixing method of
the minimum volume class. 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.
With respect to SISAL, we introduce a dimensionality estimation method based on the minimum description
length (MDL) principle. The effectiveness of the proposed algorithm is illustrated with simulated and real
data.
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 unmixing with simultaneous dimensionality estimation. ICPRAM 2012 - International Conference on Pattern Recognition Applications and Methods. 2012