Publicação
Ensemble classifiers in remote sensing: a comparative analysis
| Resumo: | 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. |
|---|---|
| Autores principais: | Rodríguez, Hernán Cortés |
| Assunto: | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| Ano: | 2014 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868983138851487744 |
|---|---|
| author | Rodríguez, Hernán Cortés |
| author_facet | Rodríguez, Hernán Cortés |
| author_role | author |
| contributor_name_str_mv | Rengel, Reyes Caetano, Mário Sílvio Rochinha de Andrade Henriques, Roberto André Pereira RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Rodríguez, Hernán Cortés\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Rengel, Reyes Caetano, Mário Sílvio Rochinha de Andrade Henriques, Roberto André Pereira RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Rodríguez, Hernán Cortés |
| datacite.date.Accepted.fl_str_mv | 2014-03-06T00:00:00Z |
| datacite.date.available.fl_str_mv | 2014-03-18T13:34:25Z |
| datacite.date.embargoed.fl_str_mv | 2014-03-18T13:34:25Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| datacite.titles.title.fl_str_mv | Ensemble classifiers in remote sensing: a comparative analysis |
| dc.contributor.none.fl_str_mv | Rengel, Reyes Caetano, Mário Sílvio Rochinha de Andrade Henriques, Roberto André Pereira RUN |
| dc.creator.none.fl_str_mv | Rodríguez, Hernán Cortés |
| dc.date.Accepted.fl_str_mv | 2014-03-06T00:00:00Z |
| dc.date.available.fl_str_mv | 2014-03-18T13:34:25Z |
| dc.date.embargoed.fl_str_mv | 2014-03-18T13:34:25Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/11671 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| dc.title.fl_str_mv | Ensemble classifiers in remote sensing: a comparative analysis |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | 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. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/c6f831c0-51db-4581-b4e9-83124558742b/download |
| id | run_ab7b33ad5d6cdc16d4e15da47d9f2bc1 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/11671 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/11671 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Rodríguez, Hernán Cortés |
| publishDate | 2014 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engporLand 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.application/pdfporEnsemble classifiers in remote sensing: a comparative analysisRodríguez, Hernán CortésRengel, ReyesCaetano, Mário Sílvio Rochinha de AndradeHenriques, Roberto André PereiraHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2013925852014-03-18T13:34:25Z2014-03-062014-03-06T00:00:00ZHandlehttp://hdl.handle.net/10362/11671http://purl.org/coar/access_right/c_abf2open accessAccuracyBaggingBoostingCARTClassifiers EnsembleLand Cover and Land Use MapsLinear Discriminant ClassifierMajority VotingNeural NetworksRandom Forest2739937 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/c6f831c0-51db-4581-b4e9-83124558742b/download |
| 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 |
| status | SINGLETON |
| subject.fl_str_mv | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| title | 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_short | Ensemble classifiers in remote sensing: a comparative analysis |
| title_sort | Ensemble classifiers in remote sensing: a comparative analysis |
| topic | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| topic_facet | Accuracy Bagging Boosting CART Classifiers Ensemble Land Cover and Land Use Maps Linear Discriminant Classifier Majority Voting Neural Networks Random Forest |
| url | http://hdl.handle.net/10362/11671 |
| visible | 1 |