Nonlinear spectral unmixing of hyperspectral images using residual component analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and 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. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Original languageEnglish
Title of host publication2014 Sensor Signal Processing for Defence
PublisherIEEE
Number of pages5
ISBN (Print)978-1-4799-5294-6
Publication statusPublished - 2014
Event4th Sensor Signal Processing for Defence 2014 - Edinburgh, Edinburgh, United Kingdom
Duration: 8 Sep 20149 Sep 2014

Conference

Conference4th Sensor Signal Processing for Defence 2014
Abbreviated titleSSPD 2014
CountryUnited Kingdom
CityEdinburgh
Period8/09/149/09/14

Keywords

  • Hyperspectral imagery
  • nonlinear spectral unmixing
  • residual component analysis
  • nonlinearity detection
  • MIXTURE ANALYSIS
  • MODEL

Cite this

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title = "Nonlinear spectral unmixing of hyperspectral images using residual component analysis",
abstract = "This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and 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. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.",
keywords = "Hyperspectral imagery, nonlinear spectral unmixing, residual component analysis, nonlinearity detection, MIXTURE ANALYSIS, MODEL",
author = "Yoann Altmann and Steve McLaughlin",
year = "2014",
language = "English",
isbn = "978-1-4799-5294-6",
booktitle = "2014 Sensor Signal Processing for Defence",
publisher = "IEEE",
address = "United States",

}

Altmann, Y & McLaughlin, S 2014, Nonlinear spectral unmixing of hyperspectral images using residual component analysis. in 2014 Sensor Signal Processing for Defence. IEEE, 4th Sensor Signal Processing for Defence 2014, Edinburgh, United Kingdom, 8/09/14.

Nonlinear spectral unmixing of hyperspectral images using residual component analysis. / Altmann, Yoann; McLaughlin, Steve.

2014 Sensor Signal Processing for Defence. IEEE, 2014.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Nonlinear spectral unmixing of hyperspectral images using residual component analysis

AU - Altmann, Yoann

AU - McLaughlin, Steve

PY - 2014

Y1 - 2014

N2 - This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and 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. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

AB - This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and 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. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

KW - Hyperspectral imagery

KW - nonlinear spectral unmixing

KW - residual component analysis

KW - nonlinearity detection

KW - MIXTURE ANALYSIS

KW - MODEL

M3 - Conference contribution

SN - 978-1-4799-5294-6

BT - 2014 Sensor Signal Processing for Defence

PB - IEEE

ER -