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Sparse representations for online-learning-based hyperspectral image compression

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dc.contributor.author Ülkü, İrem
dc.contributor.author Töreyin, Behçet Uğur
dc.date.accessioned 2017-03-09T12:25:05Z
dc.date.available 2017-03-09T12:25:05Z
dc.date.issued 2015-10-10
dc.identifier.citation Ülkü, İ., Töreyin, B.U. (2015). Sparse representations for online-learning-based hyperspectral image compression. Applied Optics, 54(29), 8625-8631. http://dx.doi.org/ 10.1364/AO.54.008625 tr_TR
dc.identifier.issn 1559-128X
dc.identifier.uri http://hdl.handle.net/20.500.12416/1419
dc.description.abstract Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio. tr_TR
dc.language.iso eng tr_TR
dc.publisher Optical Soc Amer tr_TR
dc.relation.isversionof 10.1364/AO.54.008625 tr_TR
dc.rights info:eu-repo/semantics/closedAccess
dc.subject Projections tr_TR
dc.subject Persuit tr_TR
dc.title Sparse representations for online-learning-based hyperspectral image compression tr_TR
dc.type article tr_TR
dc.relation.journal Applied Optics tr_TR
dc.contributor.authorID 17575 tr_TR
dc.contributor.authorID 19325 tr_TR
dc.identifier.volume 54 tr_TR
dc.identifier.issue 29 tr_TR
dc.identifier.startpage 8625 tr_TR
dc.identifier.endpage 8631 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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