Robust anomaly detection in hyperspectral imaging

Joana Frontera-Pons 1 Miguel Angel Veganzones 2 Santiago Velasco-Forero 3 Frédéric Pascal 1 Jean-Philippe Ovarlez 1, 4 Jocelyn Chanussot 5, 2
1 SONDRA - Laboratoire franco-singapourien de recherche en électromagnétisme et radars
2 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
3 CMM - Centre de Morphologie Mathématique
4 Palaiseau - ONERA - The French Aerospace Lab
5 University of Iceland [Reykjavik]
Abstract : Anomaly Detection methods are used when there is not enough information about the target to detect. These methods search for pixels in the image with spectral characteristics that differ from the background. The most widespread detection test, the RX-detector, is based on the Mahalanobis distance and on the background statistical characterization through the mean vector and the covariance matrix. Although non-Gaussian distributions have already been introduced for background modeling in Hyperspectral Imaging, the parameters estimation is still performed using the Maximum Likelihood Estimates for Gaussian distribution. This paper describes robust estimation procedures more suitable for non-Gaussian environment. Therefore, they can be used as plug-in estimators for the RX-detector leading to some great improvement in the detection process. This theoretical improvement has been evidenced over two real hyperspectral images.
Keywords : elliptical distributions anomaly detection hypespectral imaging M-estimators
Type de document :
Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014), Jul 2014, Québec, Canada. pp.n/c, 2014, <10.1109/IGARSS.2014.6947518>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01010418
Contributeur : Miguel Angel Veganzones <>
Soumis le : jeudi 19 juin 2014 - 16:40:39
Dernière modification le : mardi 12 septembre 2017 - 11:40:38
Document(s) archivé(s) le : vendredi 19 septembre 2014 - 11:26:44

Fichier

FronteraIGARSS_14.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

ONERA | ENSMP_CMM | ENSMP | PARISTECH | SUPELEC | SUP_E3S | SUP_SONDRA | GIPSA-SIGMAPHY | GIPSA-DIS | GIPSA | UGA | CENTRALESUPELEC | PSL

Citation

Joana Frontera-Pons, Miguel Angel Veganzones, Santiago Velasco-Forero, Frédéric Pascal, Jean-Philippe Ovarlez, et al.. Robust anomaly detection in hyperspectral imaging. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2014), Jul 2014, Québec, Canada. pp.n/c, 2014, <10.1109/IGARSS.2014.6947518>. <hal-01010418>

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de
la notice

663

Téléchargements du document

396

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD