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One-Class Support Vector Machines Based Cluster Validity in the Segmentation of Hyperspectral Images

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dc.contributor.authorBilgin, Goekhan
dc.contributor.authorErturk, Sarp
dc.contributor.authorYildirim, Tuelay
dc.contributor.institutionauthorYILDIRIM, Tülay
dc.date.accessioned2026-06-27T13:07:49Z
dc.date.issued2009
dc.description.abstractIn this paper, a novel cluster validation method based on one-class support vector machines (OC-SVM) is presented. Also it is proposed to segment hyperspectral images with subtractive clustering accompanied by phase correlation. The proposed cluster validity measure is based on the power of spectral discrimination (PWSD) measure and utilizes the advantage of the inherited cluster contour definition feature of OC-SVM. Basically this method provides a solution to the estimation of the correct number of clusters which is an important problem in hyperspectral image segmentation.en
dc.identifier.endpage+
dc.identifier.isbn978-1-4244-4435-9
dc.identifier.startpage109
dc.identifier.urihttps://hdl.handle.net/20.500.14981/50142
dc.identifier.wos000273935600028
dc.language.isotur
dc.publisherIEEE
dc.relation.conferenceIEEE 17th Signal Processing and Communications Applications Conference
dc.relation.ispartof2009 IEEE 17TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, VOLS 1 AND 2
dc.subjectComputer Science
dc.subjectEngineering
dc.subjectTelecommunications
dc.titleOne-Class Support Vector Machines Based Cluster Validity in the Segmentation of Hyperspectral Images
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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