Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection

Yoann Altmann, Nicolas Dobigeon, Steve McLaughlin, Jean-yves Tourneret

Research output: Contribution to journalArticle

Abstract

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.
Original languageEnglish
Article number6775297
Pages (from-to)2148-2158
Number of pages11
JournalIEEE Transactions on Image Processing
Volume23
Issue number5
DOIs
Publication statusPublished - 1 May 2014

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Keywords

  • Hyperspectral imagery
  • nonlinear spectral unmixing
  • nonlinearity detection
  • residual component analysis

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

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title = "Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection",
abstract = "This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.",
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Residual component analysis of hyperspectral images - Application to joint nonlinear unmixing and nonlinearity detection. / Altmann, Yoann; Dobigeon, Nicolas; McLaughlin, Steve; Tourneret, Jean-yves.

In: IEEE Transactions on Image Processing, Vol. 23, No. 5, 6775297, 01.05.2014, p. 2148-2158.

Research output: Contribution to journalArticle

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