Land cover classification from multispectral data using computational intelligence tools

Detalhes bibliográficos
Autor(a) principal: Mora, André
Data de Publicação: 2017
Outros Autores: Santos, Tiago M. A., Lukasik, Szymon, Silva, João M. N., Falcão, António J., Fonseca, José M., Ribeiro, Rita A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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).
id RCAP_cfcc33d6b9dec262e93848e384b54958
oai_identifier_str oai:run.unl.pt:10362/42119
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:34:00Zoai:run.unl.pt:10362/42119Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:04:53.516806Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596421836636160

Registros relacionados