Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

Jean Baptiste Courbot; Vincent Mazet; Emmanuel Monfrini; Christophe Collet

doi:10.1109/ICASSP.2016.7472005

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

Jean Baptiste Courbot
, Vincent Mazet
, Emmanuel Monfrini
, Christophe Collet

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

Abstract

Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.

Original language	English
Title of host publication	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1891-1895
Number of pages	5
ISBN (Electronic)	9781479999880
DOIs	https://doi.org/10.1109/ICASSP.2016.7472005
Publication status	Published - 18 May 2016
Event	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: 20 Mar 2016 → 25 Mar 2016

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2016-May
ISSN (Print)	1520-6149

Conference

Conference	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/Territory	China
City	Shanghai
Period	20/03/16 → 25/03/16

Keywords

Astronomy
Hyperspectral Data
Likelihood Ratio Test
Statistical Detection

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10.1109/ICASSP.2016.7472005

Cite this

Courbot, J. B., Mazet, V., Monfrini, E., & Collet, C. (2016). Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (pp. 1891-1895). Article 7472005 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472005

Courbot, Jean Baptiste ; Mazet, Vincent ; Monfrini, Emmanuel et al. / Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1891-1895 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations",

abstract = "Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.",

keywords = "Astronomy, Hyperspectral Data, Likelihood Ratio Test, Statistical Detection",

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series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Courbot, JB, Mazet, V, Monfrini, E & Collet, C 2016, Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings., 7472005, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2016-May, Institute of Electrical and Electronics Engineers Inc., pp. 1891-1895, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, China, 20/03/16. https://doi.org/10.1109/ICASSP.2016.7472005

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. / Courbot, Jean Baptiste; Mazet, Vincent; Monfrini, Emmanuel et al.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1891-1895 7472005 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May).

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

TY - GEN

T1 - Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

AU - Courbot, Jean Baptiste

AU - Mazet, Vincent

AU - Monfrini, Emmanuel

AU - Collet, Christophe

PY - 2016/5/18

Y1 - 2016/5/18

N2 - Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.

AB - Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.

KW - Astronomy

KW - Hyperspectral Data

KW - Likelihood Ratio Test

KW - Statistical Detection

U2 - 10.1109/ICASSP.2016.7472005

DO - 10.1109/ICASSP.2016.7472005

M3 - Conference contribution

AN - SCOPUS:84973376019

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 1891

EP - 1895

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

Y2 - 20 March 2016 through 25 March 2016

ER -

Courbot JB, Mazet V, Monfrini E, Collet C. Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1891-1895. 7472005. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2016.7472005

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

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Keywords

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