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Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery / C.C. Funk in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 39 n° 7 (July 2001) . - pp 1410 - 1420 Titre : Clustering to improve matched filter detection of weak gas plumes in hyperspectral thermal imagery Type de document : Article/Communication Auteurs : C.C. Funk, Auteur ; J. Theiler, Auteur ; C.C. Borel, Auteur ; D.A. Roberts, Auteur Année de publication : 2001 Article en page(s) : pp 1410 - 1420 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] analyse spectrale [Termes IGN] image hyperspectrale [Termes IGN] image thermique Résumé : (Auteur) The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new “extreme” centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images. Numéro de notice : A2001-200 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.934073 En ligne : https://doi.org/10.1109/36.934073 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=21894 [article]

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