Sparse Representation-based Dictionary Learning Methods for Hyperspectral Super-Resolution

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dc.contributor.authorSimsek, Murat
dc.contributor.authorPolat, Ediz
dc.date.accessioned2020-06-25T18:17:00Z
dc.date.available2020-06-25T18:17:00Z
dc.date.issued2016
dc.departmentKırıkkale Üniversitesi
dc.description24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY
dc.description.abstractDue to hardware limitations, hyperspectral imagery has low spatial resolution. It can be obtained super-resolution hyperspectral imagery by means of sparse representation-based methods that are designed for improving spatial resolution. In this paper, the effect of sparse representation-based dictionary learning algorithms including K-SVD, ODL and Bayes on obtaining superresolution images with low error and high quality has been investigated. The method with best results has been identified.en_US
dc.description.sponsorshipIEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engnen_US
dc.identifier.citationclosedAccessen_US
dc.identifier.endpage756en_US
dc.identifier.isbn978-1-5090-1679-2
dc.identifier.startpage753en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6669
dc.identifier.wosWOS:000391250900166
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIeeeen_US
dc.relation.ispartof2016 24Th Signal Processing And Communication Application Conference (Siu)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjecthyperspectral imagesen_US
dc.subjectsuper resolutionen_US
dc.subjectsparse representationen_US
dc.subjectdictionary learningen_US
dc.titleSparse Representation-based Dictionary Learning Methods for Hyperspectral Super-Resolutionen_US
dc.typeConference Object

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