Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection

Yoann Altmann, Nicolas Dobigeon, Steve McLaughlin, Jean Yves Tourneret

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

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 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 synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages3166-3170
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 1 Jan 2014
Event39th IEEE International Conference on Acoustics, Speech and Signal Processing 2014 - Florence, Italy, Florence, Italy
Duration: 4 May 20149 May 2014
http://www.icassp2014.org/home.html

Conference

Conference39th IEEE International Conference on Acoustics, Speech and Signal Processing 2014
Abbreviated titleICASSP 2014
CountryItaly
CityFlorence
Period4/05/149/05/14
OtherICASSP is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The series is sponsored by the IEEE Signal Processing Society and has been held annually since 1976. The conference features world-class speakers, tutorials, exhibits, a Show and Tell event, and over 120 lecture and poster sessions.
ICASSP is a cooperative effort of the IEEE Signal Processing Society Technical Committees:
Audio and Acoustic Signal Processing
Bio Imaging and Signal Processing
Design and Implementation of Signal Processing Systems
Image, Video, and Multidimenional Signal Processing
Information Forensics and Security
Industry DSP Technology Standing Committee
Machine Learning for Signal Processing
Multimedia Signal Processing
Sensor Array and Multichannel
Signal Processing for Communications and Networking
Signal Processing Education Standing Committee
Signal Processing Theory and Methods
Speech and Langauge Processing
Internet address

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Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Altmann, Y., Dobigeon, N., McLaughlin, S., & Tourneret, J. Y. (2014). Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 3166-3170). [6854184] IEEE. https://doi.org/10.1109/ICASSP.2014.6854184
Altmann, Yoann ; Dobigeon, Nicolas ; McLaughlin, Steve ; Tourneret, Jean Yves. / Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE, 2014. pp. 3166-3170
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title = "Residual component analysis of hyperspectral images for 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 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 synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.",
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Altmann, Y, Dobigeon, N, McLaughlin, S & Tourneret, JY 2014, Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6854184, IEEE, pp. 3166-3170, 39th IEEE International Conference on Acoustics, Speech and Signal Processing 2014 , Florence, Italy, 4/05/14. https://doi.org/10.1109/ICASSP.2014.6854184

Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. / Altmann, Yoann; Dobigeon, Nicolas; McLaughlin, Steve; Tourneret, Jean Yves.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE, 2014. p. 3166-3170 6854184.

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

TY - GEN

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N2 - 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 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 synthetic and 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 joint hyperspectral image unmixing and nonlinearity detection. 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 synthetic and real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

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KW - nonlinearity detection

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Altmann Y, Dobigeon N, McLaughlin S, Tourneret JY. Residual component analysis of hyperspectral images for joint nonlinear unmixing and nonlinearity detection. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE. 2014. p. 3166-3170. 6854184 https://doi.org/10.1109/ICASSP.2014.6854184