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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
17/11/2015 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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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, IC/Unicamp; JÚLIO CÉSAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; CLAUDIA BAUZER MEDEIROS, IC/UNICAMP. |
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Título: |
SiRCub - Brazilian Agricultural Crop Recognition System. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015. |
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Páginas: |
p. 6273-6280. |
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Idioma: |
Inglês |
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Notas: |
SBSR 2015. |
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Conteúdo: |
This paper presents a novel approach to classify agricultural crops using NDVI time series. The novelty lies in i) extracting a set of features from the each and every NDVI curve, and ii) using them to train a crop classification model using a Support Vector Machine (SVM). Specifically, we use the TIMESAT program package to: 1) smooth the time series, 2) decompose them into agricultural seasons?a season is the period between sowing and harvesting?, and 3) extract the features for each season. |
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Palavras-Chave: |
Cobertura da terra; LULC; NDVI; Séries temporais; Support Vector Machine. |
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Thesagro: |
Uso da terra. |
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Thesaurus Nal: |
Land cover; Land use; Time series analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/133123/1/SBSR-Tomas.pdf
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Marc: |
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001 2028693
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008 2015 bl uuuu u00u1 u #d
100 1 $aTOMÀS, J. C.
245 $aSiRCub - Brazilian Agricultural Crop Recognition System.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE$c2015
300 $ap. 6273-6280.
500 $aSBSR 2015.
520 $aThis paper presents a novel approach to classify agricultural crops using NDVI time series. The novelty lies in i) extracting a set of features from the each and every NDVI curve, and ii) using them to train a crop classification model using a Support Vector Machine (SVM). Specifically, we use the TIMESAT program package to: 1) smooth the time series, 2) decompose them into agricultural seasons?a season is the period between sowing and harvesting?, and 3) extract the features for each season.
650 $aLand cover
650 $aLand use
650 $aTime series analysis
650 $aUso da terra
653 $aCobertura da terra
653 $aLULC
653 $aNDVI
653 $aSéries temporais
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.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
