Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/21179 |
Resumo: | Satellite images from remote sensors appear as viable and efficient alternatives in the study of information on patterns of land use and coverage. Data extracted from satellite images are currently used in machine learning, and this method is able to predict the class of new data in the domain in which it was trained.Thus, the present study aimed to analyze the capacity of machine learning algorithms to predict land use and land cover in the municipality of Dona Francisca - RS. The geographic database was implemented in the QGIS software, where the import of TM/Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019. Subsequently, the synthetic composition of the false bands RGB color 543 from Landsat 5 and RGB 654 Landsat 8, in order to obtain the samples of the reference pixels, taking into consideration the spectral information of each pixel (numerical value), in order to obtain information to characterize and differentiate patterns of land use and coverage (water, agriculture, countryside, forest and exposed soil). After the training and testing of the algorithms started in the proportions of 80% - 20%, 70% -30%, 60% -40% through the machine learning algorithms Random Forest (RF), Support Vector Machine (SVM) , K-Nearest Neighbors (KNN) and Naive Bayes (NB) in the KNIME software, and finally presented the performance of the global accuracy and the Kappa index. The results showed that the RF and SVM machine learning algorithms showed the best performances for the years 2004 and 2009. As for the year 2015, the KNN and RF algorithms had a better overall accuracy. The NB algorithm showed lower performance in all tests than the other studied algorithms. The Kappa index values generated by the KNIME software indicate that the quality of the classifications generated by the RF, SVM, KNN and NB algorithms for all years were from very good to excellent. It is evident that the machine learning algorithms showed satisfactory results, so that they were efficient in predicting land use and land cover from data from orbital images. |
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Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIMEMachine learning procedures for analysis of spatial patterns with the use of the KNIME platformInteligência artificialKNIMESensoriamento remotoLandsatArtificial intelligenceRemote sensingCNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTALSatellite images from remote sensors appear as viable and efficient alternatives in the study of information on patterns of land use and coverage. Data extracted from satellite images are currently used in machine learning, and this method is able to predict the class of new data in the domain in which it was trained.Thus, the present study aimed to analyze the capacity of machine learning algorithms to predict land use and land cover in the municipality of Dona Francisca - RS. The geographic database was implemented in the QGIS software, where the import of TM/Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019. Subsequently, the synthetic composition of the false bands RGB color 543 from Landsat 5 and RGB 654 Landsat 8, in order to obtain the samples of the reference pixels, taking into consideration the spectral information of each pixel (numerical value), in order to obtain information to characterize and differentiate patterns of land use and coverage (water, agriculture, countryside, forest and exposed soil). After the training and testing of the algorithms started in the proportions of 80% - 20%, 70% -30%, 60% -40% through the machine learning algorithms Random Forest (RF), Support Vector Machine (SVM) , K-Nearest Neighbors (KNN) and Naive Bayes (NB) in the KNIME software, and finally presented the performance of the global accuracy and the Kappa index. The results showed that the RF and SVM machine learning algorithms showed the best performances for the years 2004 and 2009. As for the year 2015, the KNN and RF algorithms had a better overall accuracy. The NB algorithm showed lower performance in all tests than the other studied algorithms. The Kappa index values generated by the KNIME software indicate that the quality of the classifications generated by the RF, SVM, KNN and NB algorithms for all years were from very good to excellent. It is evident that the machine learning algorithms showed satisfactory results, so that they were efficient in predicting land use and land cover from data from orbital images.Imagens de satélites provenientes dos sensores remotos surgem como alternativas viáveis e eficientes no estudo de informações sobre os padrões do uso e cobertura da terra. Dados extraídos das imagens de satélite, são atualmente empregados no aprendizado de máquina, sendo este método capaz de prever a classe de novos dados do domínio no qual ele foi treinado. Desta forma, o presente trabalho teve como objetivo analisar a capacidade dos algoritmos de aprendizado de máquina em predizer o uso e cobertura da terra do município de Dona Francisca - RS. O banco de dados geográficos foi implementado no software QGIS, onde iniciou-se a importação das imagens TM/Landsat 5 nos anos de 2004 e 2009 e OLI/Landsat 8 para os anos de 2015 e 2019. Posteriormente realizou-se a composição sintética das bandas falsa cor RGB 543 do Landsat 5 e RGB 654 Landsat 8, com a finalidade de obter as amostras dos pixels de referência, levando em consideração a informação espectral de cada pixel (valor numérico), com a finalidade de obter informações para caracterizar e diferenciar os padrões de uso e cobertura da terra (água, agricultura, campo, floresta e solo exposto). Após iniciou-se o treinamento e teste dos algoritmos nas proporções de 80%-20%, 70%- 30%, 60%-40% por meio dos algoritmos de aprendizado de máquina Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) e Naive Bayes (NB) no software KNIME, e por fim apresentado o desempenho da acurácia global e do índice de Kappa. Os resultados evidenciaram que os algoritmos de aprendizado de máquina RF e SVM apresentaram os melhores desempenhos para os anos de 2004 e 2009. Já para o ano de 2015, os algoritmos KNN e a RF tiveram uma acurácia global melhor. O algoritmo NB demostrou em todos os testes desempenhos inferiores aos demais algoritmos estudados. Os valores do índice Kappa gerados pelo software KNIME indicam que a qualidade das classificações geradas pelos algoritmos RF, SVM, KNN e NB para todos os anos foram de muito boa a excelente. Evidencia-se que os algoritmos de aprendizado de máquina mostraram resultados satisfatórios, de maneira que apresentaram eficiência em predizer o uso e cobertura da terra a partir de dados provenientes das imagens orbitais.Universidade Federal de Santa MariaBrasilRecursos Florestais e Engenharia FlorestalUFSMPrograma de Pós-Graduação em Engenharia FlorestalCentro de Ciências RuraisPereira, Rudiney Soareshttp://lattes.cnpq.br/9479801378014588Padilha, Damaris GonçalvesXXXXXXXXXXXXXXXSilva, Emanuel AraújoXXXXXXXXXXXXXXXXXXFantinel, Roberta Aparecida2021-06-22T18:47:23Z2021-06-22T18:47:23Z2020-02-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/21179porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2021-06-23T06:01:48Zoai:repositorio.ufsm.br:1/21179Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2021-06-23T06:01:48Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME Machine learning procedures for analysis of spatial patterns with the use of the KNIME platform |
title |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
spellingShingle |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME Fantinel, Roberta Aparecida Inteligência artificial KNIME Sensoriamento remoto Landsat Artificial intelligence Remote sensing CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
title_short |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
title_full |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
title_fullStr |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
title_full_unstemmed |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
title_sort |
Procedimentos de aprendizagem de máquina para análise de padrões espaciais com o uso da plataforma KNIME |
author |
Fantinel, Roberta Aparecida |
author_facet |
Fantinel, Roberta Aparecida |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pereira, Rudiney Soares http://lattes.cnpq.br/9479801378014588 Padilha, Damaris Gonçalves XXXXXXXXXXXXXXX Silva, Emanuel Araújo XXXXXXXXXXXXXXXXXX |
dc.contributor.author.fl_str_mv |
Fantinel, Roberta Aparecida |
dc.subject.por.fl_str_mv |
Inteligência artificial KNIME Sensoriamento remoto Landsat Artificial intelligence Remote sensing CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
topic |
Inteligência artificial KNIME Sensoriamento remoto Landsat Artificial intelligence Remote sensing CNPQ::CIENCIAS AGRARIAS::RECURSOS FLORESTAIS E ENGENHARIA FLORESTAL |
description |
Satellite images from remote sensors appear as viable and efficient alternatives in the study of information on patterns of land use and coverage. Data extracted from satellite images are currently used in machine learning, and this method is able to predict the class of new data in the domain in which it was trained.Thus, the present study aimed to analyze the capacity of machine learning algorithms to predict land use and land cover in the municipality of Dona Francisca - RS. The geographic database was implemented in the QGIS software, where the import of TM/Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019. Subsequently, the synthetic composition of the false bands RGB color 543 from Landsat 5 and RGB 654 Landsat 8, in order to obtain the samples of the reference pixels, taking into consideration the spectral information of each pixel (numerical value), in order to obtain information to characterize and differentiate patterns of land use and coverage (water, agriculture, countryside, forest and exposed soil). After the training and testing of the algorithms started in the proportions of 80% - 20%, 70% -30%, 60% -40% through the machine learning algorithms Random Forest (RF), Support Vector Machine (SVM) , K-Nearest Neighbors (KNN) and Naive Bayes (NB) in the KNIME software, and finally presented the performance of the global accuracy and the Kappa index. The results showed that the RF and SVM machine learning algorithms showed the best performances for the years 2004 and 2009. As for the year 2015, the KNN and RF algorithms had a better overall accuracy. The NB algorithm showed lower performance in all tests than the other studied algorithms. The Kappa index values generated by the KNIME software indicate that the quality of the classifications generated by the RF, SVM, KNN and NB algorithms for all years were from very good to excellent. It is evident that the machine learning algorithms showed satisfactory results, so that they were efficient in predicting land use and land cover from data from orbital images. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-20 2021-06-22T18:47:23Z 2021-06-22T18:47:23Z |
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://repositorio.ufsm.br/handle/1/21179 |
url |
http://repositorio.ufsm.br/handle/1/21179 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Recursos Florestais e Engenharia Florestal UFSM Programa de Pós-Graduação em Engenharia Florestal Centro de Ciências Rurais |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Recursos Florestais e Engenharia Florestal UFSM Programa de Pós-Graduação em Engenharia Florestal Centro de Ciências Rurais |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1805922107706572800 |