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Hyperspectral Image Classification Using Spatial Features Extracted by Fuzzy C-Means and Dirichlet Mixture Model

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dc.contributor.authorSigirci, Ibrahim Onur
dc.contributor.authorBilgin, Gokhan
dc.date.accessioned2026-06-27T13:58:01Z
dc.date.issued2016
dc.description.abstractThe spectral content of the small number of training data may not be enough for the classification of high-dimensional hyperspectral images. For this reason, spatial information is also exploited next to the spectral information. In this study, it is intended to classify hyperspectral images using spatial features extracted by fuzzy C-means (FCM) and Dirichlet Mixture Model (DMM). The contribution of the cascaded use of proposed methods are presented in the results section by tables.en
dc.identifier.endpage900
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.startpage897
dc.identifier.urihttps://hdl.handle.net/20.500.14981/56018
dc.identifier.wos000391250900201
dc.language.isotur
dc.publisherIEEE
dc.relation.conference24th Signal Processing and Communication Application Conference (SIU)
dc.relation.ispartof2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU)
dc.subjecthyperspectral images
dc.subjectfuzzy c-means
dc.subjectDirichlet mixture model
dc.subjectspectral feature
dc.subjectspatial feature
dc.subjectEngineering
dc.titleHyperspectral Image Classification Using Spatial Features Extracted by Fuzzy C-Means and Dirichlet Mixture Model
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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