NDLI: A post nonlinear mixing model for hyperspectral images unmixing

Content Provider	IEEE Xplore Digital Library
Author	Altmann, Y. Halimi, A. Dobigeon, N. Tourneret, J.-Y.
Copyright Year	2011
Description	Author affiliation: University of Toulouse, IRIT-ENSEEIHT, France (Altmann, Y.; Halimi, A.; Dobigeon, N.; Tourneret, J.-Y.)
Abstract	This paper studies estimation algorithms for nonlinear hyperspectral image unmixing. The proposed unmixing model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. A hierarchical Bayesian algorithm and an optimization method are proposed for solving the resulting unmixing problem. The parameters involved in the proposed model satisfy constraints that are naturally included in the estimation procedure. The performance of the unmixing strategies is evaluated thanks to simulations conducted on synthetic and real data.
Starting Page	1882
Ending Page	1885
File Size	431224
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049491
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Bayesian methods Polynomials Signal processing algorithms Estimation Taylor approximation Post nonlinear mixing model hyperspectral images MCMC methods
Content Type	Text
Resource Type	Article

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A post nonlinear mixing model for hyperspectral images unmixing