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dc.contributor.authorLeón Navarro, Germán
dc.contributor.authorGonzález, Carlos
dc.contributor.authorMayo, Rafael
dc.contributor.authorMozos, Daniel
dc.contributor.authorQuintana Ortí, Enrique S.
dc.date.accessioned2019-06-28T11:29:19Z
dc.date.available2019-06-28T11:29:19Z
dc.date.issued2019-05
dc.identifier.citationLeón, G., González, C., Mayo, R. et al. J Supercomput (2019) 75: 1323.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/183035
dc.description.abstractWe present a reliable and efficient FPGA implementation of a procedure for the computation of the noise estimation matrix, a key stage for subspace identification of hyperspectral images. Our hardware realization is based on numerically stable orthogonal transformations, avoids the numerical difficulties of the normal equations method for the solution of linear least squares problems (LLS), and exploits the special relations between coupled LLS problems arising in the hyperspectral image. Our modular implementation decomposes the QR factorization that comprises a significant part of the cost into a sequence of suboperations, which can be efficiently computed on an FPGA.ca_CA
dc.format.extent13 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2018ca_CA
dc.subjecthyperspectral imagesca_CA
dc.subjectsubspace identificationca_CA
dc.subjectnoise estimationca_CA
dc.subjectleast squares problemsca_CA
dc.titleNoise estimation for hyperspectral subspace identification on FPGAsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s11227-018-2425-3
dc.relation.projectIDMINECO (Projects TIN2014-53495-R and TIN2013-40968-P)ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s11227-018-2425-3ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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