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An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion

 

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dc.creator García Gutiérrez, Jorge es
dc.creator Mateos García, Daniel es
dc.creator García, Mariano es
dc.creator Riquelme Santos, José Cristóbal es
dc.date.accessioned 2016-07-14T09:57:56Z
dc.date.available 2016-07-14T09:57:56Z
dc.date.issued 2015
dc.identifier.citation García Gutiérrez, J., Mateos García, D., García, M. y Riquelme Santos, J.C. (2015). An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion. Neurocomputing, 163, 17-24.
dc.identifier.issn 0925-2312 es
dc.identifier.uri http://hdl.handle.net/11441/43603
dc.description.abstract Data classification is a critical step to convert remotely sensed data into thematic information. Environmental researchers have recently maximized the synergy between passive sensors and LiDAR (Light Detection and Ranging) for land cover classification by means of machine learning. Although object-based paradigm is frequently used to classify high resolution imagery, it often requires a high level of expertise and time effort. Contextual classification may lead to similar results with a decrease in time and costs for non-expert users. This work shows a novel contextual classifier based on a Support Vector Machine (SVM) and an Evolutionary Majority Voting (SVM–EMV) to develop thematic maps from LiDAR and imagery data. Subsequently, the performance of SVM–EMV is compared to that achieved by a pixel-based SVM as well as to a contextual classified based on SVM and MRF. The classifiers were tested over three different areas of Spain with well differentiated environmental characteristics. Results show that SVM-EMV statistically outperforms the rest (SVM, SVM–MRF) for the three datasets obtaining a 77%, 91% and 92% of global accuracy for Trabada, Huelva and Alto Tajo, respectively. es
dc.description.sponsorship Xunta de Galicia CSO2010-15807 es
dc.description.sponsorship Ministerio de Ciencia y Tecnología TIN2011-28956-C02 es
dc.description.sponsorship Junta de Andalucia P11-TIC-7528 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Elsevier es
dc.relation.ispartof Neurocomputing, 163, 17-24.
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Contextual classification es
dc.subject Data fusion es
dc.subject LiDAR es
dc.subject Machine learning es
dc.subject Multispectral es
dc.subject Remote sensing es
dc.title An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/acceptedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.rights.accessrights info: eu-repo/semantics/embargoAccess
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.relation.projectID CSO2010-15807 es
dc.relation.projectID TIN2011-28956-C02 es
dc.relation.projectID P11-TIC-7528 es
dc.date.embargoEndDate 2017-09-02
dc.identifier.doi http://dx.doi.org/10.1016/j.neucom.2014.08.086 es
idus.format.extent 8 es
dc.journaltitle Neurocomputing es
dc.publication.volumen 163 es
dc.publication.initialPage 17 es
dc.publication.endPage 24 es
dc.identifier.idus https://idus.us.es/xmlui/handle/11441/43603
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