Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

Yoann Altmann; Nicolas Dobigeon; Steve McLaughlin; Jean-Yves Tourneret

doi:10.1109/ICASSP.2012.6288115

Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

Yoann Altmann^*, Nicolas Dobigeon, Steve McLaughlin, Jean-Yves Tourneret

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.

Original language	English
Title of host publication	2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	IEEE
Pages	1249-1252
Number of pages	4
ISBN (Print)	9781467300469
DOIs	https://doi.org/10.1109/ICASSP.2012.6288115
Publication status	Published - 31 Aug 2012
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing - Kyoto, Japan Duration: 25 Mar 2012 → 30 Mar 2012

Conference

Conference	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2012
Country/Territory	Japan
City	Kyoto
Period	25/03/12 → 30/03/12

Keywords

Gaussian Processes
hyperspectral images
Nonlinear unmixing

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2012.6288115

Cite this

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title = "Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes",

abstract = "This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.",

keywords = "Gaussian Processes, hyperspectral images, Nonlinear unmixing",

author = "Yoann Altmann and Nicolas Dobigeon and Steve McLaughlin and Jean-Yves Tourneret",

year = "2012",

month = aug,

day = "31",

doi = "10.1109/ICASSP.2012.6288115",

language = "English",

isbn = "9781467300469",

pages = "1249--1252",

booktitle = "2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

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note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",

}

Altmann, Y, Dobigeon, N, McLaughlin, S & Tourneret, J-Y 2012, Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes. in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1249-1252, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto, Japan, 25/03/12. https://doi.org/10.1109/ICASSP.2012.6288115

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T1 - Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

AU - Altmann, Yoann

AU - Dobigeon, Nicolas

AU - McLaughlin, Steve

AU - Tourneret, Jean-Yves

PY - 2012/8/31

Y1 - 2012/8/31

N2 - This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.

AB - This paper describes a Gaussian process based method for nonlinear hyperspectral image unmixing. The proposed model assumes a nonlinear mapping from the abundance vectors to the pixel reflectances contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy physical constraints that are naturally expressed within a Bayesian framework. The proposed abundance estimation procedure is applied simultaneously to all pixels of the image by maximizing an appropriate posterior distribution which does not depend on the endmembers. After determining the abundances of all image pixels, the endmembers contained in the image are estimated by using Gaussian process regression. The performance of the resulting unsupervised unmixing strategy is evaluated through simulations conducted on synthetic data.

KW - Gaussian Processes

KW - hyperspectral images

KW - Nonlinear unmixing

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U2 - 10.1109/ICASSP.2012.6288115

DO - 10.1109/ICASSP.2012.6288115

M3 - Conference contribution

AN - SCOPUS:84867604334

SN - 9781467300469

SP - 1249

EP - 1252

BT - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 25 March 2012 through 30 March 2012

ER -

Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

Abstract

Conference

Keywords

ASJC Scopus subject areas

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