Land cover classification from multispectral data using computational intelligence tools
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://doi.org/10.3390/info8040147 |
Resumo: | This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of the Center of Technologies and System (CTS) of UNINOVA-Institute for the Development of new Technologies. It is also partially based on work from COST Action TD1403 "Big Data Era in Sky and Earth Observation", supported by COST (European Cooperation in Science and Technology). |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Land cover classification from multispectral data using computational intelligence toolsA comparative studyAggregation operatorsImage fusionLand cover classificationRemote sensingInformation SystemsThis work was partially funded by FCT Strategic Program UID/EEA/00066/203 of the Center of Technologies and System (CTS) of UNINOVA-Institute for the Development of new Technologies. It is also partially based on work from COST Action TD1403 "Big Data Era in Sky and Earth Observation", supported by COST (European Cooperation in Science and Technology).This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasRUNMora, AndréSantos, Tiago M. A.Lukasik, SzymonSilva, João M. N.Falcão, António J.Fonseca, José M.Ribeiro, Rita A.2018-07-20T22:14:05Z2017-11-152017-11-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://doi.org/10.3390/info8040147eng2078-2489PURE: 3679539http://www.scopus.com/inward/record.url?scp=85036460977&partnerID=8YFLogxKhttps://doi.org/10.3390/info8040147info: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:RCAAP2024-03-11T04:22:45Zoai:run.unl.pt:10362/42119Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:31:27.138048Repositório 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 |
Land cover classification from multispectral data using computational intelligence tools A comparative study |
title |
Land cover classification from multispectral data using computational intelligence tools |
spellingShingle |
Land cover classification from multispectral data using computational intelligence tools Mora, André Aggregation operators Image fusion Land cover classification Remote sensing Information Systems |
title_short |
Land cover classification from multispectral data using computational intelligence tools |
title_full |
Land cover classification from multispectral data using computational intelligence tools |
title_fullStr |
Land cover classification from multispectral data using computational intelligence tools |
title_full_unstemmed |
Land cover classification from multispectral data using computational intelligence tools |
title_sort |
Land cover classification from multispectral data using computational intelligence tools |
author |
Mora, André |
author_facet |
Mora, André Santos, Tiago M. A. Lukasik, Szymon Silva, João M. N. Falcão, António J. Fonseca, José M. Ribeiro, Rita A. |
author_role |
author |
author2 |
Santos, Tiago M. A. Lukasik, Szymon Silva, João M. N. Falcão, António J. Fonseca, José M. Ribeiro, Rita A. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
CTS - Centro de Tecnologia e Sistemas UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias RUN |
dc.contributor.author.fl_str_mv |
Mora, André Santos, Tiago M. A. Lukasik, Szymon Silva, João M. N. Falcão, António J. Fonseca, José M. Ribeiro, Rita A. |
dc.subject.por.fl_str_mv |
Aggregation operators Image fusion Land cover classification Remote sensing Information Systems |
topic |
Aggregation operators Image fusion Land cover classification Remote sensing Information Systems |
description |
This work was partially funded by FCT Strategic Program UID/EEA/00066/203 of the Center of Technologies and System (CTS) of UNINOVA-Institute for the Development of new Technologies. It is also partially based on work from COST Action TD1403 "Big Data Era in Sky and Earth Observation", supported by COST (European Cooperation in Science and Technology). |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-11-15 2017-11-15T00:00:00Z 2018-07-20T22:14:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://doi.org/10.3390/info8040147 |
url |
https://doi.org/10.3390/info8040147 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2078-2489 PURE: 3679539 http://www.scopus.com/inward/record.url?scp=85036460977&partnerID=8YFLogxK https://doi.org/10.3390/info8040147 |
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 |
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1799137937961517056 |