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dc.contributor.authorLatorre Carmona, Pedro
dc.contributor.authorMartínez Sotoca, José
dc.contributor.authorPla Bañón, Filiberto
dc.contributor.authorBioucas-Dias, José
dc.contributor.authorJulià Ferré, Carme
dc.date.accessioned2014-03-20T17:26:36Z
dc.date.available2014-03-20T17:26:36Z
dc.date.issued2013
dc.identifier.citationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013ca_CA
dc.identifier.issn1939-1404
dc.identifier.urihttp://hdl.handle.net/10234/87989
dc.description.abstractThis paper presents a comparative analysis of six band selection methods applied to hyperspectral datasets for biophysical variable estimation problems, where the effect of denoising on band selection performance has also been analyzed. In particular, we consider four hyperspectral datasets and three regressors of different nature ("􀀀�SVR, Regression Trees, and Kernel Ridge Regression). Results show that the denoising approach improves the band selection quality of all the tested methods. We show that noise filtering is more beneficial for the selection methods that use an estimator based on the whole dataset for the prediction of the output than for methods that use strategies based on local information (neighboring points).ca_CA
dc.format.extent10 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)ca_CA
dc.relation.isPartOfIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2013, vol. 6, no 2ca_CA
dc.rights© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.ca_CA
dc.subjectFeature selectionca_CA
dc.subjectHyperspectral datasetsca_CA
dc.subjectNoiseca_CA
dc.subjectRegressionca_CA
dc.titleEffect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasetsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1109/JSTARS.2013.2241022
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6461428ca_CA


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