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| Registros recuperados : 143 | |
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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: |
14/09/2021 |
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Data da última atualização: |
14/09/2021 |
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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: |
SILVA, J. P. da; ZULLO JÚNIOR, J.; ROMANI, L. A. S. |
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Afiliação: |
JOÃO PAULO DA SILVA, Feagri/Unicamp; JURANDIR ZULLO JÚNIOR, UNICAMP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA. |
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Título: |
A time series mining approach for agricultural area detection. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
IEEE Transactions on Big Data, v. 6, n. 3, p. 537-546, Sept. 2020. |
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DOI: |
10.1109/TBDATA.2019.2913402 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract-Acquiring meaningful data to be employed in building training sets for classification models is a costly task, both in terms of difficult to find suitable samples as well as their quantity. In this sense, Active Learning (AL) improves the training set building by providing an efficient way to select only essential data to be attached to the training set, consequently reducing its size and even enhancing model's accuracy, when compared to random sample selection. In this paper, we proposed a framework for time series classification in order to monitor sugarcane area in Sao Paulo, Brazil. The AL approach consisted of selecting seasonal time series information from less than 1 percent of each class' pixels to build the training set and evaluate this selection by an expert user supported by distance measurements, repeating this process until both distance measurement thresholds were satisfied. In most years, the classification results presented about 90 percent of correlation with official estimates based on both traditional and satellite image analysis methods. This framework can then help Land Use Change (LUC) monitoring as it produced similar results compared to other methods that demands more human and financial resources to be adopted. |
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Palavras-Chave: |
Active Learning; Análise de séries temporais; Classificação de pixel; Pixel classification. |
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Thesagro: |
Agricultura; Meio Ambiente; Sensoriamento Remoto. |
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Thesaurus NAL: |
Agriculture; Environment; Remote sensing; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02133naa a2200289 a 4500
001 2134329
005 2021-09-14
008 2020 bl uuuu u00u1 u #d
024 7 $a10.1109/TBDATA.2019.2913402$2DOI
100 1 $aSILVA, J. P. da
245 $aA time series mining approach for agricultural area detection.$h[electronic resource]
260 $c2020
520 $aAbstract-Acquiring meaningful data to be employed in building training sets for classification models is a costly task, both in terms of difficult to find suitable samples as well as their quantity. In this sense, Active Learning (AL) improves the training set building by providing an efficient way to select only essential data to be attached to the training set, consequently reducing its size and even enhancing model's accuracy, when compared to random sample selection. In this paper, we proposed a framework for time series classification in order to monitor sugarcane area in Sao Paulo, Brazil. The AL approach consisted of selecting seasonal time series information from less than 1 percent of each class' pixels to build the training set and evaluate this selection by an expert user supported by distance measurements, repeating this process until both distance measurement thresholds were satisfied. In most years, the classification results presented about 90 percent of correlation with official estimates based on both traditional and satellite image analysis methods. This framework can then help Land Use Change (LUC) monitoring as it produced similar results compared to other methods that demands more human and financial resources to be adopted.
650 $aAgriculture
650 $aEnvironment
650 $aRemote sensing
650 $aTime series analysis
650 $aAgricultura
650 $aMeio Ambiente
650 $aSensoriamento Remoto
653 $aActive Learning
653 $aAnálise de séries temporais
653 $aClassificação de pixel
653 $aPixel classification
700 1 $aZULLO JÚNIOR, J.
700 1 $aROMANI, L. A. S.
773 $tIEEE Transactions on Big Data$gv. 6, n. 3, p. 537-546, Sept. 2020.
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