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Classifiers ensemble in remote sensing: a comparative analysis
dc.contributor | Grangel Seguer, Reyes | |
dc.contributor | Caetano, Mário | |
dc.contributor | Henriques, Roberto | |
dc.contributor.author | Cortés Rodríguez, Hernán | |
dc.contributor.other | Universitat Jaume I. Departament de Llenguatges i Sistemes Informàtics | |
dc.date.accessioned | 2016-04-18T07:04:33Z | |
dc.date.available | 2016-04-18T07:04:33Z | |
dc.date.issued | 2014-04 | |
dc.identifier.uri | http://hdl.handle.net/10234/158826 | |
dc.description | Treball final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2013). Codi: SIW013. Curs acadèmic 2013-2014 | ca_CA |
dc.description.abstract | Land Cover and Land Use (LCLU) maps are very important tools for understanding the relationships between human activities and the natural environment. Defining accurately all the features over the Earth's surface is essential to assure their management properly. The basic data which are being used to derive those maps are remote sensing imagery (RSI), and concretely, satellite images. Hence, new techniques and methods able to deal with those data and at the same time, do it accurately, have been demanded. In this work, our goal was to have a brief review over some of the currently approaches in the scientific community to face this challenge, to get higher accuracy in LCLU maps. Although, we will be focus on the study of the classifiers ensembles and the different strategies that those ensembles present in the literature. We have proposed different ensembles strategies based in our data and previous work, in order to increase the accuracy of previous LCLU maps made by using the same data and single classifiers. Finally, only one of the ensembles proposed have got significantly higher accuracy, in the classification of LCLU map, than the better single classifier performance with the same data. Also, it was proved that diversity did not play an important role in the success of this ensemble. | ca_CA |
dc.format.extent | 66 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Universitat Jaume I | ca_CA |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 Spain | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | ca_CA |
dc.subject | Erasmus Mundus University Master's Degree in Geospatial Technologies | ca_CA |
dc.subject | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | ca_CA |
dc.subject | accuracy | ca_CA |
dc.subject | bagging | ca_CA |
dc.subject | Boosting | ca_CA |
dc.subject | CART | ca_CA |
dc.subject | Classifiers Ensemble | ca_CA |
dc.subject | Diversity | ca_CA |
dc.subject | Feature Selection | ca_CA |
dc.subject | Land Cover and Land Use Maps | ca_CA |
dc.subject | Linear Discriminant Classifier | ca_CA |
dc.subject | Majority Voting | ca_CA |
dc.subject | Neural Networks | ca_CA |
dc.subject | Random Forest | ca_CA |
dc.subject | Regularized Discriminant Classifier | ca_CA |
dc.subject | Remote Sensing Imagery | ca_CA |
dc.subject | Stacked Description | ca_CA |
dc.subject | Single Classifiers | ca_CA |
dc.subject | Support Vector Machine | ca_CA |
dc.title | Classifiers ensemble in remote sensing: a comparative analysis | ca_CA |
dc.type | info:eu-repo/semantics/masterThesis | ca_CA |
dc.educationLevel | Estudios de Postgrado | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
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TFM: Màster Universitari Erasmus Mundus en Tecnologia Geoespacial [79]
SIW013; SIK013