Improving land cover classification using genetic programming for feature construction

Detalhes bibliográficos
Autor(a) principal: Batista, João E.
Data de Publicação: 2021
Outros Autores: Cabral, Ana I. R., Vasconcelos, Maria J. P., Vanneschi, Leonardo, Silva, Sara
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/117837
Resumo: Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623
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spelling Improving land cover classification using genetic programming for feature constructionClassificationEvolutionary computationFeature constructionGenetic programmingHyperfeaturesMachine learningMulti-class classificationSpectral indicesEarth and Planetary Sciences(all)SDG 15 - Life on LandBatista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNBatista, João E.Cabral, Ana I. R.Vasconcelos, Maria J. P.Vanneschi, LeonardoSilva, Sara2021-05-18T00:30:40Z2021-05-012021-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/117837eng2072-4292PURE: 29765589https://doi.org/10.3390/rs13091623info: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:RCAAP2024-03-11T05:00:46Zoai:run.unl.pt:10362/117837Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:41.809255Repositório 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 Improving land cover classification using genetic programming for feature construction
title Improving land cover classification using genetic programming for feature construction
spellingShingle Improving land cover classification using genetic programming for feature construction
Batista, João E.
Classification
Evolutionary computation
Feature construction
Genetic programming
Hyperfeatures
Machine learning
Multi-class classification
Spectral indices
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
title_short Improving land cover classification using genetic programming for feature construction
title_full Improving land cover classification using genetic programming for feature construction
title_fullStr Improving land cover classification using genetic programming for feature construction
title_full_unstemmed Improving land cover classification using genetic programming for feature construction
title_sort Improving land cover classification using genetic programming for feature construction
author Batista, João E.
author_facet Batista, João E.
Cabral, Ana I. R.
Vasconcelos, Maria J. P.
Vanneschi, Leonardo
Silva, Sara
author_role author
author2 Cabral, Ana I. R.
Vasconcelos, Maria J. P.
Vanneschi, Leonardo
Silva, Sara
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Batista, João E.
Cabral, Ana I. R.
Vasconcelos, Maria J. P.
Vanneschi, Leonardo
Silva, Sara
dc.subject.por.fl_str_mv Classification
Evolutionary computation
Feature construction
Genetic programming
Hyperfeatures
Machine learning
Multi-class classification
Spectral indices
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
topic Classification
Evolutionary computation
Feature construction
Genetic programming
Hyperfeatures
Machine learning
Multi-class classification
Spectral indices
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
description Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623
publishDate 2021
dc.date.none.fl_str_mv 2021-05-18T00:30:40Z
2021-05-01
2021-05-01T00:00:00Z
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 http://hdl.handle.net/10362/117837
url http://hdl.handle.net/10362/117837
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
PURE: 29765589
https://doi.org/10.3390/rs13091623
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 25
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
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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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