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Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding

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dc.contributor.author Ülkü, İrem
dc.contributor.author Töreyin, Behçet Uğur
dc.date.accessioned 2020-04-19T23:52:51Z
dc.date.available 2020-04-19T23:52:51Z
dc.date.issued 2014
dc.identifier.citation Ulku, 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). tr_TR
dc.identifier.uri http://hdl.handle.net/20.500.12416/3378
dc.description.abstract A 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. tr_TR
dc.language.iso eng tr_TR
dc.publisher IEEE tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Sparse Coding tr_TR
dc.subject Hyperspectral Imagery tr_TR
dc.subject Anomaly Detection tr_TR
dc.subject Online Learning tr_TR
dc.title Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) tr_TR
dc.contributor.authorID 17575 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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