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dc.contributor.authorTOMÀS, J. C.pt_BR
dc.contributor.authorFARIA, F. A.pt_BR
dc.contributor.authorESQUERDO, J. C. D. M.pt_BR
dc.contributor.authorCOUTINHO, A. C.pt_BR
dc.contributor.authorMEDEIROS, C. B.pt_BR
dc.date.accessioned2015-11-17T11:11:11Zpt_BR
dc.date.available2015-11-17T11:11:11Zpt_BR
dc.date.created2015-11-17pt_BR
dc.date.issued2015pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 17., 2015, João Pessoa. Anais... São José dos Campos: INPE, 2015.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1028693pt_BR
dc.descriptionThis 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.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectSéries temporaispt_BR
dc.subjectLULCpt_BR
dc.subjectCobertura da terrapt_BR
dc.subjectNDVIpt_BR
dc.subjectSupport Vector Machinept_BR
dc.titleSiRCub - Brazilian Agricultural Crop Recognition System.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2020-01-21T11:11:11Zpt_BR
dc.subject.thesagroUso da terrapt_BR
dc.subject.nalthesaurusTime series analysispt_BR
dc.subject.nalthesaurusLand usept_BR
dc.subject.nalthesaurusLand coverpt_BR
dc.description.notesSBSR 2015.pt_BR
dc.format.extent2p. 6273-6280.pt_BR
riaa.ainfo.id1028693pt_BR
riaa.ainfo.lastupdate2020-01-21 -02:00:00pt_BR
dc.contributor.institutionJORDI CREUS TOMÀS, IC/Unicamp; FABIO AUGUSTO FARIA, IC/Unicamp; JÚLIO CÉSAR DALLA MORA ESQUERDO, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; CLAUDIA BAUZER MEDEIROS, IC/UNICAMP.pt_BR
Aparece en las colecciones:Artigo em anais de congresso (CNPTIA)

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