DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES

Detalhes bibliográficos
Autor(a) principal: Becker,Willyan R.
Data de Publicação: 2017
Outros Autores: Johann,Jerry A., Richetti,Jonathan, Silva,Laíza C. DE A.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Engenharia Agrícola
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400750
Resumo: ABSTRACT: Due to the difficulty in discriminating soybean and corn in mappings obtained by the time series of satellite images, this study aimed to apply the data mining techniques to separate soybean and corn. Pure pixels selection from Landsat-8 were extracted and used to build a standard spectro-temporal EVI profile for both crops. These profiles were obtained with the Timesat software and, further incorporated in the Weka software. Five out of eleven variables of the standard spectro-temporal EVI profile for each crop were found through the decision tree, a data mining technique. These five variables were sufficient to achieve the separation of soybean and corn crops with an accuracy of 96.3% and a kappa index of 0.92.
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spelling DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGEScornEVIJ48soybeanWekaABSTRACT: Due to the difficulty in discriminating soybean and corn in mappings obtained by the time series of satellite images, this study aimed to apply the data mining techniques to separate soybean and corn. Pure pixels selection from Landsat-8 were extracted and used to build a standard spectro-temporal EVI profile for both crops. These profiles were obtained with the Timesat software and, further incorporated in the Weka software. Five out of eleven variables of the standard spectro-temporal EVI profile for each crop were found through the decision tree, a data mining technique. These five variables were sufficient to achieve the separation of soybean and corn crops with an accuracy of 96.3% and a kappa index of 0.92.Associação Brasileira de Engenharia Agrícola2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400750Engenharia Agrícola v.37 n.4 2017reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v37n4p750-759/2017info:eu-repo/semantics/openAccessBecker,Willyan R.Johann,Jerry A.Richetti,JonathanSilva,Laíza C. DE A.eng2017-10-17T00:00:00Zoai:scielo:S0100-69162017000400750Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2017-10-17T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false
dc.title.none.fl_str_mv DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
title DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
spellingShingle DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
Becker,Willyan R.
corn
EVI
J48
soybean
Weka
title_short DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
title_full DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
title_fullStr DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
title_full_unstemmed DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
title_sort DATA MINING TECHNIQUES FOR SEPARATION OF SUMMER CROP BASED ON SATELLITE IMAGES
author Becker,Willyan R.
author_facet Becker,Willyan R.
Johann,Jerry A.
Richetti,Jonathan
Silva,Laíza C. DE A.
author_role author
author2 Johann,Jerry A.
Richetti,Jonathan
Silva,Laíza C. DE A.
author2_role author
author
author
dc.contributor.author.fl_str_mv Becker,Willyan R.
Johann,Jerry A.
Richetti,Jonathan
Silva,Laíza C. DE A.
dc.subject.por.fl_str_mv corn
EVI
J48
soybean
Weka
topic corn
EVI
J48
soybean
Weka
description ABSTRACT: Due to the difficulty in discriminating soybean and corn in mappings obtained by the time series of satellite images, this study aimed to apply the data mining techniques to separate soybean and corn. Pure pixels selection from Landsat-8 were extracted and used to build a standard spectro-temporal EVI profile for both crops. These profiles were obtained with the Timesat software and, further incorporated in the Weka software. Five out of eleven variables of the standard spectro-temporal EVI profile for each crop were found through the decision tree, a data mining technique. These five variables were sufficient to achieve the separation of soybean and corn crops with an accuracy of 96.3% and a kappa index of 0.92.
publishDate 2017
dc.date.none.fl_str_mv 2017-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400750
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162017000400750
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1809-4430-eng.agric.v37n4p750-759/2017
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
publisher.none.fl_str_mv Associação Brasileira de Engenharia Agrícola
dc.source.none.fl_str_mv Engenharia Agrícola v.37 n.4 2017
reponame:Engenharia Agrícola
instname:Associação Brasileira de Engenharia Agrícola (SBEA)
instacron:SBEA
instname_str Associação Brasileira de Engenharia Agrícola (SBEA)
instacron_str SBEA
institution SBEA
reponame_str Engenharia Agrícola
collection Engenharia Agrícola
repository.name.fl_str_mv Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)
repository.mail.fl_str_mv revistasbea@sbea.org.br||sbea@sbea.org.br
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