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dc.contributor.authorPerea Moreno, Alberto Jesús
dc.contributor.authorMeroño de Larriva, José Emilio
dc.contributor.authorAguilera Ureña, M. Jesús
dc.date.accessioned2013-05-03T08:12:14Z
dc.date.available2013-05-03T08:12:14Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10396/9938
dc.description.abstractThe objective of this paper was the development of a methodology for the classification of digital aerial images, which, with the aid of object-based classification and the Normalized Difference Vegetation Index (NDVI), can quantify agricultural areas, by using algorithms of expert classification, with the aim of improving the final results of thematic classifications. QuickBird satellite images and data of 2532 plots in Hinojosa del Duque, Spain, were used to validate the different classifications, obtaining an overall classification accuracy of 91.9% and an excellent Kappa statistic (87.6%) for the algorithm of expert classificationes_ES
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoenges_ES
dc.publisherInstituto de Investigaciones Agropecuarias (Chile)es_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.sourceChilean Journal of Agricultural Research 69 (3), 400-405 (2009)es_ES
dc.subjectExpert classificationes_ES
dc.subjectVegetation indexes_ES
dc.subjectLand coveres_ES
dc.subjectObject-based classificationes_ES
dc.titleAlgorithms of expert classification applied in quickbird satellite images for land use mappinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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