Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery

Yoann Altmann, Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret

Research output: Contribution to journalArticle

Abstract

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model. The performance of the unmixing strategies is evaluated thanks to simulations conducted on synthetic and real data.
Original languageEnglish
Pages (from-to)3017-3025
JournalIEEE Transactions on Image Processing
Volume21
Issue number6
DOIs
Publication statusPublished - Jun 2012

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imagery
reflectance
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abstract = "This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using polynomial functions leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm and optimization methods are proposed to estimate the parameters involved in the model. The performance of the unmixing strategies is evaluated thanks to simulations conducted on synthetic and real data.",
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Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery. / Altmann, Yoann; Halimi, Abderrahim; Dobigeon, Nicolas; Tourneret, Jean-Yves.

In: IEEE Transactions on Image Processing, Vol. 21, No. 6, 06.2012, p. 3017-3025.

Research output: Contribution to journalArticle

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