Ensemble classifiers in remote sensing: a comparative analysis

Detalhes bibliográficos
Autor(a) principal: Rodríguez, Hernán Cortés
Data de Publicação: 2014
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/11671
Resumo: Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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spelling Ensemble classifiers in remote sensing: a comparative analysisAccuracyBaggingBoostingCARTClassifiers EnsembleLand Cover and Land Use MapsLinear Discriminant ClassifierMajority VotingNeural NetworksRandom ForestDissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.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.Rengel, ReyesCaetano, Mário Sílvio Rochinha de AndradeHenriques, Roberto André PereiraRUNRodríguez, Hernán Cortés2014-03-18T13:34:25Z2014-03-062014-03-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/11671TID:201392585enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:15:37Zoai:run.unl.pt:10362/11671Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:15:37Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Ensemble classifiers in remote sensing: a comparative analysis
title Ensemble classifiers in remote sensing: a comparative analysis
spellingShingle Ensemble classifiers in remote sensing: a comparative analysis
Rodríguez, Hernán Cortés
Accuracy
Bagging
Boosting
CART
Classifiers Ensemble
Land Cover and Land Use Maps
Linear Discriminant Classifier
Majority Voting
Neural Networks
Random Forest
title_short Ensemble classifiers in remote sensing: a comparative analysis
title_full Ensemble classifiers in remote sensing: a comparative analysis
title_fullStr Ensemble classifiers in remote sensing: a comparative analysis
title_full_unstemmed Ensemble classifiers in remote sensing: a comparative analysis
title_sort Ensemble classifiers in remote sensing: a comparative analysis
author Rodríguez, Hernán Cortés
author_facet Rodríguez, Hernán Cortés
author_role author
dc.contributor.none.fl_str_mv Rengel, Reyes
Caetano, Mário Sílvio Rochinha de Andrade
Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Rodríguez, Hernán Cortés
dc.subject.por.fl_str_mv Accuracy
Bagging
Boosting
CART
Classifiers Ensemble
Land Cover and Land Use Maps
Linear Discriminant Classifier
Majority Voting
Neural Networks
Random Forest
topic Accuracy
Bagging
Boosting
CART
Classifiers Ensemble
Land Cover and Land Use Maps
Linear Discriminant Classifier
Majority Voting
Neural Networks
Random Forest
description Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
publishDate 2014
dc.date.none.fl_str_mv 2014-03-18T13:34:25Z
2014-03-06
2014-03-06T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/11671
TID:201392585
url http://hdl.handle.net/10362/11671
identifier_str_mv TID:201392585
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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