Unmixing hyperspectral images using the generalized bilinear model

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

doi:10.1109/IGARSS.2011.6049492

Unmixing hyperspectral images using the generalized bilinear model

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

^*Corresponding author for this work

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

59 Citations (Scopus)

Abstract

Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

Original language	English
Title of host publication	2011 IEEE International Geoscience and Remote Sensing Symposium
Publisher	IEEE
Pages	1886-1889
Number of pages	4
ISBN (Electronic)	9781457710056
ISBN (Print)	9781457710032
DOIs	https://doi.org/10.1109/IGARSS.2011.6049492
Publication status	Published - 20 Oct 2011
Event	2011 IEEE International Geoscience and Remote Sensing Symposium - Vancouver, BC, Canada Duration: 24 Jul 2011 → 29 Jul 2011

Conference

Conference	2011 IEEE International Geoscience and Remote Sensing Symposium
Abbreviated title	IGARSS 2011
Country/Territory	Canada
City	Vancouver, BC
Period	24/07/11 → 29/07/11

Keywords

Bayesian inference
bilinear model
gradient descent algorithm
hyperspectral imagery
least square algorithm
MCMC methods
spectral unmixing

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

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10.1109/IGARSS.2011.6049492

Cite this

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title = "Unmixing hyperspectral images using the generalized bilinear model",

abstract = "Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.",

keywords = "Bayesian inference, bilinear model, gradient descent algorithm, hyperspectral imagery, least square algorithm, MCMC methods, spectral unmixing",

author = "Abderrahim Halimi and Yoann Altmann and Nicolas Dobigeon and Jean-Yves Tourneret",

year = "2011",

month = oct,

day = "20",

doi = "10.1109/IGARSS.2011.6049492",

language = "English",

isbn = "9781457710032",

pages = "1886--1889",

booktitle = "2011 IEEE International Geoscience and Remote Sensing Symposium",

publisher = "IEEE",

address = "United States",

note = "2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011 ; Conference date: 24-07-2011 Through 29-07-2011",

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T1 - Unmixing hyperspectral images using the generalized bilinear model

AU - Halimi, Abderrahim

AU - Altmann, Yoann

AU - Dobigeon, Nicolas

AU - Tourneret, Jean-Yves

PY - 2011/10/20

Y1 - 2011/10/20

N2 - Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

AB - Nonlinear models have recently shown interesting properties for spectral unmixing. This paper considers a generalized bilinear model recently introduced for unmixing hyperspectral images. Different algorithms are studied to estimate the parameters of this bilinear model. The positivity and sum-to-one constraints for the abundances are ensured by the proposed algorithms. The performance of the resulting unmixing strategy is evaluated via simulations conducted on synthetic and real data.

KW - Bayesian inference

KW - bilinear model

KW - gradient descent algorithm

KW - hyperspectral imagery

KW - least square algorithm

KW - MCMC methods

KW - spectral unmixing

UR - http://www.scopus.com/inward/record.url?scp=80955122660&partnerID=8YFLogxK

U2 - 10.1109/IGARSS.2011.6049492

DO - 10.1109/IGARSS.2011.6049492

M3 - Conference contribution

AN - SCOPUS:80955122660

SN - 9781457710032

SP - 1886

EP - 1889

BT - 2011 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE

T2 - 2011 IEEE International Geoscience and Remote Sensing Symposium

Y2 - 24 July 2011 through 29 July 2011

ER -

Unmixing hyperspectral images using the generalized bilinear model

Abstract

Conference

Keywords

ASJC Scopus subject areas

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