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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/72803
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dc.contributor.authorPisani, R.-
dc.contributor.authorRiedel, P.-
dc.contributor.authorGomes, A.-
dc.contributor.authorMizobe, R.-
dc.contributor.authorPapa, J.-
dc.date.accessioned2014-05-27T11:26:07Z-
dc.date.accessioned2016-10-25T18:35:29Z-
dc.date.available2014-05-27T11:26:07Z-
dc.date.available2016-10-25T18:35:29Z-
dc.date.issued2011-11-16-
dc.identifierhttp://dx.doi.org/10.1109/IGARSS.2011.6050183-
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), p. 4304-4307.-
dc.identifier.urihttp://hdl.handle.net/11449/72803-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/72803-
dc.description.abstractIn this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.en
dc.format.extent4304-4307-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectmoist area classification-
dc.subjectoptimum-path forest-
dc.subjectremote sensing-
dc.subjectArea classification-
dc.subjectClassification procedure-
dc.subjectLarge datasets-
dc.subjectOff-line training-
dc.subjectPattern recognition algorithms-
dc.subjectPruning algorithms-
dc.subjectRadar image-
dc.subjectTraining sets-
dc.subjectAlgorithms-
dc.subjectGeology-
dc.subjectImage analysis-
dc.subjectImage classification-
dc.subjectPattern recognition-
dc.subjectRadar-
dc.subjectRemote sensing-
dc.subjectClassification (of information)-
dc.titleIs it possible to make pixel-based radar image classification user-friendly?en
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP - Univ. Estadual Paulista Geosciences and Exact Sciences Institute-
dc.description.affiliationUNESP - Univ. Estadual Paulista Department of Computing-
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Geosciences and Exact Sciences Institute-
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Department of Computing-
dc.identifier.doi10.1109/IGARSS.2011.6050183-
dc.identifier.wosWOS:000297496304067-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)-
dc.identifier.scopus2-s2.0-80955168675-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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