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Hyperspectral image compression using an online learning method

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dc.contributor.author Ülkü, İrem
dc.contributor.author Töreyin, B. Uğur
dc.date.accessioned 2022-05-23T11:28:41Z
dc.date.available 2022-05-23T11:28:41Z
dc.date.issued 2015
dc.identifier.citation Ülkü, İrem; Töreyin, B. Uğur (2015). "Hyperspectral image compression using an online learning method", Proceedings of SPIE - The International Society for Optical Engineering, Vol. 9501. tr_TR
dc.identifier.isbn 9781628416176
dc.identifier.issn 0277-786X
dc.identifier.uri http://hdl.handle.net/20.500.12416/5535
dc.description.abstract A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes. © 2015 SPIE. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1117/12.2178133 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Basis Pursuit tr_TR
dc.subject Hyperspectral Compression tr_TR
dc.subject Hyperspectral Imagery tr_TR
dc.subject Online Learning tr_TR
dc.subject Sparse Coding tr_TR
dc.title Hyperspectral image compression using an online learning method tr_TR
dc.type conferenceObject tr_TR
dc.relation.journal Proceedings of SPIE - The International Society for Optical Engineering tr_TR
dc.contributor.authorID 19325 tr_TR
dc.identifier.volume 9501 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü tr_TR


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