NDLI: Library-based linear unmixing for hyperspectral imagery via reversible jump MCMC sampling

Content Provider	IEEE Xplore Digital Library
Author	Dobigeon, N. Tourneret, J.-Y.
Copyright Year	2009
Description	Author affiliation: University of Michigan, Department of EECS, Ann Arbor, 48109-2122, USA (Dobigeon, N.) \|\| University of Toulouse, IRIT/INP-ENSEEIHT, BP 7122, 31071 Toulouse cedex 7, FRANCE (Tourneret, J.-Y.)
Abstract	This paper studies a semi-supervised algorithm for linear hyperspectral unmixing. The proposed unmixing method assumes that the pure material spectra denoted as endmembers belong to a library that is a priori available. However, the number and the nature of endmembers appearing in the pixel are not known a priori, resulting in a model selection problem. This paper proposes to handle this model selection problem within a fully Bayesian framework. First, appropriate distributions are elected as prior distributions for the unknown parameters. Particularly, a distribution defined on a simplex is chosen as prior for an appropriate partial abundance vector to ensure the positivity and the sum-to-one constraints of the mixing coefficients. Due to the complexity of the posterior distribution, a reversible jump Markov chain Monte Carlo algorithm is proposed to estimate the number and the nature of the macroscopic materials, as well as their respective proportions in the pixel. The accuracy of the proposed method is illustrated by simulations on synthetic hyperspectral data.
Starting Page	1
Ending Page	6
File Size	777885
Page Count	6
File Format	PDF
ISBN	9781424426218
ISSN	1095323X
DOI	10.1109/AERO.2009.4839492
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Image sampling Bayesian methods Libraries Monte Carlo methods Layout Stochastic processes Vectors Space exploration Geoscience
Content Type	Text
Resource Type	Article
Subject	Aerospace Engineering Space and Planetary Science

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