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 | |