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dc.contributor.authorTuna, Süha
dc.contributor.authorTöreyin, Behçet Uğur
dc.contributor.authorDemiralp, Metin
dc.contributor.authorRen, Jinchang
dc.contributor.authorZhao, Huimin
dc.contributor.authorMarshall, Stephen
dc.date.accessioned2021-11-30T12:38:29Z
dc.date.available2021-11-30T12:38:29Z
dc.date.issuedNovember 2021en_US
dc.identifier.citationTUNA, Süha, Behçet Uğur TÖREYİN, Metin DEMİRALP, Jinchang REN, Huimin ZHAO & Stephen MARSHALL. "Iterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Images". IEEE Transactions on Geoscience and Remote Sensing, 59.11 November (2021): 9569-9584.en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/9258418
dc.identifier.urihttps://hdl.handle.net/11352/3984
dc.description.abstractEffective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/TGRS.2020.3031016en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectClassification accuracyen_US
dc.subjectEnhanced multivariance products representation (EMPR)en_US
dc.subjectHyperspectral (HS) imagesen_US
dc.subjectJPEG2000en_US
dc.subjectLossy compressionen_US
dc.titleIterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Imagesen_US
dc.typearticleen_US
dc.relation.journalIEEE Transactions on Geoscience and Remote Sensingen_US
dc.contributor.departmentFSM Vakıf Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.contributor.authorIDhttps://orcid.org/ 0000-0002-9492-6896en_US
dc.contributor.authorIDhttps://orcid.org/ 0000-0001-6116-3194en_US
dc.contributor.authorIDhttps://orcid.org/ 0000-0001-7079-5628en_US
dc.identifier.volume59en_US
dc.identifier.issue11en_US
dc.identifier.startpage9569en_US
dc.identifier.endpage9584en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorTuna, Süha


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