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| 3. |  | TOMÀS, J. C.; FARIA, F. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; MEDEIROS, C. B. SiRCub: a novel approach to recognize agricultural crops using supervised classification. International Journal of Agricultural and Environmental Information Systems, v. 8, n. 4, p. 20-36, Oct./Dec. 2017. Publicado também em: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (Ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019. v. II, chap. 50, p. 1129-1147. DOI:...| Biblioteca(s): Embrapa Agricultura Digital. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
20/12/2017 |
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Data da última atualização: |
12/02/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
TOMÀS, J. C.; FARIA, F. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; MEDEIROS, C. B. |
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Afiliação: |
JORDI CREUS TOMÀS, IC/Unicamp; FABIO AUGUSTO FARIA, Federal University of São Paulo, São José dos Campos; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; CLAUDIA BAUZER MEDEIROS, IC/Unicamp. |
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Título: |
SiRCub: a novel approach to recognize agricultural crops using supervised classification. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
International Journal of Agricultural and Environmental Information Systems, v. 8, n. 4, p. 20-36, Oct./Dec. 2017. |
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DOI: |
10.4018/IJAEIS.2017100102 |
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Idioma: |
Inglês |
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Notas: |
Publicado também em: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (Ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019. v. II, chap. 50, p. 1129-1147. DOI: 10.4018/978-1-5225-7033-2.ch050. |
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Conteúdo: |
This paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles. |
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Palavras-Chave: |
Aprendizado de máquina; Crop classification; Enhanced Vegetation Index; Índice de vegetação; LULC; Machine Learning; Séries temporais; SIRCub; Support Vector Machine. |
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Thesagro: |
Sensoriamento remoto; Uso da terra. |
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Thesaurus NAL: |
Artificial intelligence; Land cover; Land use; normalized difference vegetation index; Remote sensing; Time series analysis; Vegetation index. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02556naa a2200409 a 4500
001 2082972
005 2020-02-12
008 2017 bl uuuu u00u1 u #d
024 7 $a10.4018/IJAEIS.2017100102$2DOI
100 1 $aTOMÀS, J. C.
245 $aSiRCub$ba novel approach to recognize agricultural crops using supervised classification.$h[electronic resource]
260 $c2017
500 $aPublicado também em: INFORMATION RESOURCES MANAGEMENT ASSOCIATION (Ed.). Environmental information systems: concepts, methodologies, tools, and applications. Hershey: IGI Global, 2019. v. II, chap. 50, p. 1129-1147. DOI: 10.4018/978-1-5225-7033-2.ch050.
520 $aThis paper presents a new approach to deal with agricultural crop recognition using SVM (Support Vector Machine), applied to time series of NDVI images. The presented method can be divided into two steps. First, the Timesat software package is used to extract a set of crop features from the NDVI time series. These features serve as descriptors that characterize each NDVI vegetation curve, i.e., the period comprised between sowing and harvesting dates. Then, it is used an SVM to learn the patterns that define each type of crop, and create a crop model that allows classifying new series. The authors present a set of experiments that show the effectiveness of this technique. They evaluated their algorithm with a collection of more than 3000 time series from the Brazilian State of Mato Grosso spanning 4 years (2009-2013). Such time series were annotated in the field by specialists from Embrapa (Brazilian Agricultural Research Corporation). This methodology is generic, and can be adapted to distinct regions and crop profiles.
650 $aArtificial intelligence
650 $aLand cover
650 $aLand use
650 $anormalized difference vegetation index
650 $aRemote sensing
650 $aTime series analysis
650 $aVegetation index
650 $aSensoriamento remoto
650 $aUso da terra
653 $aAprendizado de máquina
653 $aCrop classification
653 $aEnhanced Vegetation Index
653 $aÍndice de vegetação
653 $aLULC
653 $aMachine Learning
653 $aSéries temporais
653 $aSIRCub
653 $aSupport Vector Machine
700 1 $aFARIA, F. A.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aCOUTINHO, A. C.
700 1 $aMEDEIROS, C. B.
773 $tInternational Journal of Agricultural and Environmental Information Systems$gv. 8, n. 4, p. 20-36, Oct./Dec. 2017.
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