Robust Detection using M- estimators for Hyperspectral Imaging

Joana Frontera-Pons 1 Mélanie Mahot 1 Jean-Philippe Ovarlez 1, 2 Frédéric Pascal 1 Jocelyn Chanussot 3
1 SONDRA - Sondra, CentraleSupélec, Université Paris-Saclay
2 ONERA - The French Aerospace Lab [Palaiseau]
3 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : Hyperspectral data have been proved not to be multivariate normal but long tailed distributed. In order to take into account these features, the family of elliptical contoured distributions is proposed to describe noise statistical behavior. Although non-Gaussian models are assumed for background modeling and detectors design, the parameters estimation is still performed using classical Gaussian based estimators; as for the covariance matrix, generally determined according to the SCM approach. We discuss here the class of M-estimators as a robust alternative for background statistical characterization and highlight their outcome when used in an adaptive GLRT-LQ detector.
Document type :
Conference papers
Complete list of metadatas
https://hal-supelec.archives-ouvertes.fr/hal-00780777
Contributor : Anne-Hélène Picot <>
Submitted on : Thursday, January 24, 2013 - 5:54:29 PM
Last modification on : Friday, June 21, 2019 - 11:18:04 AM

Identifiers

Collections

SUPELEC | SUP_E3S | SUP_SONDRA | ONERA | GIPSA-SIGMAPHY | GIPSA | GIPSA-DIS | UGA | UGA-TEST-TER

Citation

Joana Frontera-Pons, Mélanie Mahot, Jean-Philippe Ovarlez, Frédéric Pascal, Jocelyn Chanussot. Robust Detection using M- estimators for Hyperspectral Imaging. 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2012) , Jun 2012, Shangai, China. ⟨10.1109/WHISPERS.2012.6874335⟩. ⟨hal-00780777⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

483

Contact

CCSD