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Sparse coding of hyperspectral imagery using online learning

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dc.contributor.author Ülkü, İrem
dc.contributor.author Töreyin, Behçet Uğur
dc.date.accessioned 2017-03-09T12:53:43Z
dc.date.available 2017-03-09T12:53:43Z
dc.date.issued 2015-05
dc.identifier.citation Ülkü, İ., Töreyin, B.U. (2015). Sparse coding of hyperspectral imagery using online learning. Signal Image And Video Processing, 9(4), 959-966. http://dx.doi.org/10.1007/s11760-015-0753-9 tr_TR
dc.identifier.issn 1863-1703
dc.identifier.uri http://hdl.handle.net/20.500.12416/1423
dc.description.abstract Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques. tr_TR
dc.language.iso eng tr_TR
dc.publisher Springer tr_TR
dc.relation.isversionof 10.1007/s11760-015-0753-9 tr_TR
dc.rights info:eu-repo/semantics/closedAccess
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 Sparse coding of hyperspectral imagery using online learning tr_TR
dc.type article tr_TR
dc.relation.journal Signal Image And Video Processing tr_TR
dc.contributor.authorID 17575 tr_TR
dc.contributor.authorID 19325 tr_TR
dc.identifier.volume 9 tr_TR
dc.identifier.issue 4 tr_TR
dc.identifier.startpage 959 tr_TR
dc.identifier.endpage 966 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü tr_TR


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