Robust linear spectral unmixing using outlier detection

Yoann Altmann; Steve McLaughlin; Alfred Hero

doi:10.1109/ICASSP.2015.7178414

Robust linear spectral unmixing using outlier detection

Yoann Altmann, Steve McLaughlin, Alfred Hero

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

3 Citations (Scopus)

Abstract

This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.

Original language	English
Title of host publication	Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Publisher	IEEE
Pages	2464-2468
Number of pages	5
Volume	2015-August
ISBN (Print)	9781467369978
DOIs	https://doi.org/10.1109/ICASSP.2015.7178414
Publication status	Published - 4 Aug 2015
Event	40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015 - Brisbane, Australia Duration: 19 Apr 2015 → 24 Apr 2015

Conference

Conference	40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015
Abbreviated title	ICASSP 2015
Country/Territory	Australia
City	Brisbane
Period	19/04/15 → 24/04/15

Keywords

Bayesian estimation
Hyperspectral imagery
MCMC
nonlinearity detection
unsupervised spectral unmixing

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2015.7178414

Cite this

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title = "Robust linear spectral unmixing using outlier detection",

abstract = "This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.",

keywords = "Bayesian estimation, Hyperspectral imagery, MCMC, nonlinearity detection, unsupervised spectral unmixing",

author = "Yoann Altmann and Steve McLaughlin and Alfred Hero",

year = "2015",

month = aug,

day = "4",

doi = "10.1109/ICASSP.2015.7178414",

language = "English",

isbn = "9781467369978",

volume = "2015-August",

pages = "2464--2468",

booktitle = "Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on",

publisher = "IEEE",

address = "United States",

note = "40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015, ICASSP 2015 ; Conference date: 19-04-2015 Through 24-04-2015",

}

Altmann, Y , McLaughlin, S & Hero, A 2015, Robust linear spectral unmixing using outlier detection. in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. vol. 2015-August, 7178414, IEEE, pp. 2464-2468, 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015, Brisbane, Australia, 19/04/15. https://doi.org/10.1109/ICASSP.2015.7178414

TY - GEN

T1 - Robust linear spectral unmixing using outlier detection

AU - Altmann, Yoann

AU - McLaughlin, Steve

AU - Hero, Alfred

PY - 2015/8/4

Y1 - 2015/8/4

N2 - This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.

AB - This paper presents a Bayesian algorithm for linear spectral unmixing that accounts for outliers present in the data. The proposed model assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional term modelling outliers and additive Gaussian noise. A Markov random field is considered for outlier detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and outlier detection algorithm. Simulations conducted with synthetic data demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images.

KW - Bayesian estimation

KW - Hyperspectral imagery

KW - MCMC

KW - nonlinearity detection

KW - unsupervised spectral unmixing

UR - http://www.scopus.com/inward/record.url?scp=84946094266&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2015.7178414

DO - 10.1109/ICASSP.2015.7178414

M3 - Conference contribution

AN - SCOPUS:84946094266

SN - 9781467369978

VL - 2015-August

SP - 2464

EP - 2468

BT - Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on

PB - IEEE

T2 - 40th IEEE International Conference on Acoustics, Speech and Signal Processing 2015

Y2 - 19 April 2015 through 24 April 2015

ER -

Robust linear spectral unmixing using outlier detection

Abstract

Conference

Keywords

ASJC Scopus subject areas

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