A Probabilistic Method for the Classification of Hyperspectral Images

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dc.contributor.authorKutluk, Sezer
dc.contributor.authorKayabol, Koray
dc.contributor.authorAkan, Aydin
dc.date.accessioned2025-10-29T11:37:33Z
dc.date.issued2016
dc.departmentFakülteler, Mühendislik Fakültesi, Elektronik Mühendisliği Bölümü
dc.description24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY
dc.description.abstractIn this study a supervised classification and dimensionality reduction method for hyperspectral images is proposed. For this purpose, using probabilistic principal component analysis (PPCA), dimensionality reduction is performed and a Gaussian mixture model (GMM) is built. Alongside this mixture model, spatial information is also included into the classification process by taking advantage of pixel neighborhoods.
dc.description.sponsorshipIEEE,Bulent Ecevit Univ, Dept Elect & Elect Engn,Bulent Ecevit Univ, Dept Biomed Engn,Bulent Ecevit Univ, Dept Comp Engn
dc.identifier.endpage908
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.issn#DEĞER!
dc.identifier.orcid0000-0001-8894-5794
dc.identifier.scopus2-s2.0-84982836765
dc.identifier.scopusqualityN/A
dc.identifier.startpage905
dc.identifier.urihttps://hdl.handle.net/20.500.14854/13905
dc.identifier.wosWOS:000391250900203
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2016 24th Signal Processing and Communication Application Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20251020
dc.subjecthyperspectral image
dc.subjectprobabilistic principal component analysis
dc.subjectdimensionality reduction
dc.subjectmixture models
dc.titleA Probabilistic Method for the Classification of Hyperspectral Images
dc.typeConference Object

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