Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
17/08/2011 |
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Data da última atualização: |
24/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
ANTUNES, J. F. G.; RODRIGUES, L. H. A.; OLIVEIRA, S. R. de M. |
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Afiliação: |
JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; L. H. A. RODRIGUES, Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA. |
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Título: |
Data mining for sugarcane crop classification using MODIS data. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE, FOOD; THE ENVIRONMENT WORLD CONGRESS ON COMPUTERS IN AGRICULTURE, 8., 2011, Prague. Proceedings... Prague: Czech University of Life Sciences Prague, 2011. |
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Páginas: |
p. 55-66. |
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ISBN: |
978-80-904830-3-3 |
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Idioma: |
Inglês |
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Notas: |
EFITA/WCCA 2011. |
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Conteúdo: |
MODIS (Moderate Resolution Imaging Spectroradiometer) data provide coverage of large areas and high periodicity. These characteristics are fundamental for monitoring strategic agricultural crops in Brazil, such as sugarcane. Data mining is a promising approach to enhance remote sensing data analysis. The objective of this work was to apply the decision tree induction technique to classify sugarcane crop in São Paulo, Brazil, by using MODIS data. A classification model with a good accuracy has been obtained. Furthermore, such an induction technique allowed for knowledge discovery through decision rules that are relevant for specialists. The results revealed the adherence of data mining techniques to satellite image classification problems. |
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Palavras-Chave: |
Árvore de decisão; Cana-de-açúcar em São Paulo; Dados MODIS; Data mining; Decision tree; Mineração de dados; Recuperação da informação. |
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Thesagro: |
Sensoriamento Remoto. |
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Thesaurus NAL: |
Information retrieval; Remote sensing; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01813nam a2200301 a 4500
001 1898177
005 2020-01-24
008 2011 bl uuuu u00u1 u #d
020 $a978-80-904830-3-3
100 1 $aANTUNES, J. F. G.
245 $aData mining for sugarcane crop classification using MODIS data.$h[electronic resource]
260 $aIn: EUROPEAN FEDERATION FOR INFORMATION TECHNOLOGY IN AGRICULTURE, FOOD; THE ENVIRONMENT WORLD CONGRESS ON COMPUTERS IN AGRICULTURE, 8., 2011, Prague. Proceedings... Prague: Czech University of Life Sciences Prague$c2011
300 $ap. 55-66.
500 $aEFITA/WCCA 2011.
520 $aMODIS (Moderate Resolution Imaging Spectroradiometer) data provide coverage of large areas and high periodicity. These characteristics are fundamental for monitoring strategic agricultural crops in Brazil, such as sugarcane. Data mining is a promising approach to enhance remote sensing data analysis. The objective of this work was to apply the decision tree induction technique to classify sugarcane crop in São Paulo, Brazil, by using MODIS data. A classification model with a good accuracy has been obtained. Furthermore, such an induction technique allowed for knowledge discovery through decision rules that are relevant for specialists. The results revealed the adherence of data mining techniques to satellite image classification problems.
650 $aInformation retrieval
650 $aRemote sensing
650 $aSugarcane
650 $aSensoriamento Remoto
653 $aÁrvore de decisão
653 $aCana-de-açúcar em São Paulo
653 $aDados MODIS
653 $aData mining
653 $aDecision tree
653 $aMineração de dados
653 $aRecuperação da informação
700 1 $aRODRIGUES, L. H. A.
700 1 $aOLIVEIRA, S. R. de M.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
