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Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding

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dc.contributor.authorÜlkü, İrem
dc.contributor.authorTöreyin, Behçet Uğur
dc.contributor.authorID17575tr_TR
dc.date.accessioned2020-04-19T23:52:51Z
dc.date.available2020-04-19T23:52:51Z
dc.date.issued2014
dc.departmentÇankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.en_US
dc.identifier.citationUlku, Irem; Toreyin, B. Ugur, "Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding", International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), (2014).en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12416/3378
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofInternational Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSparse Codingen_US
dc.subjectHyperspectral Imageryen_US
dc.subjectAnomaly Detectionen_US
dc.subjectOnline Learningen_US
dc.titleLossy Compression of Hyperspectral Images Using Online Learning Based Sparse Codingtr_TR
dc.titleLossy Compression of Hyperspectral Images Using Online Learning Based Sparse Codingen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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