Centre de documentation
scientifique

Bayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1969 - 1981 Titre : Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Christopher Ratto, Auteur ; Kenneth D. Morton, Auteur ; Leslie M. Collins, Auteur ; Peter A. Torrione, Auteur Année de publication : 2014 Article en page(s) : pp 1969 - 1981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification contextuelle [Termes IGN] détection d'objet [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] rayonnement infrarouge Résumé : (Auteur) Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. Numéro de notice : A2014-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257175 En ligne : https://doi.org/10.1109/TGRS.2013.2257175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33170 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014041	RAB	Revue	Centre de documentation	En réserve L003	Disponible