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@InProceedings{BendiniFonKorSanMar:2017:EvSmMe,
               author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and 
                         Korting, Thales Sehn and Sanches, Ieda Del Arco and Marujo, Rennan 
                         de Freitas Bezerra",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Evaluation of smoothing methods on Landsat-8 EVI time series for 
                         crop classification based on phenological parameters",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4267--4274",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This study aims to evaluate three different time series smoothing 
                         methods, combined or not with filtering techniques, and their 
                         impact on the agricultural land use classification, in a region of 
                         the Brazilian Cerrado, using phenological parameters extracted 
                         from Enhanced Vegetation Index (EVI) Landsat-8 image time series. 
                         We extracted the time series from pixels located on well know 
                         polygons delimited on agricultural lands and monitored on a field 
                         campaign during August 2015 and August 2016. For the 
                         classification we considered the following classes: Annual 
                         Agriculture, Natural Forest, Perennial Agriculture, Semi-Perennial 
                         Agriculture and Grassland. The three smoothing algorithms were 
                         implemented through the TIMESAT software package including the: 
                         Savitzky-Golay (SG), asymmetric Gaussian function (AG) and 
                         double-logistic function (DL), and then the phenological 
                         attributes were extracted. For each method the phenological 
                         attributes were subjected to data mining using the Random Forest 
                         (RF) algorithm. The results were evaluated by the confusion matrix 
                         analysis, including global accuracy, producerīs accuracy and 
                         kappa. The intra-class variability was measured by calculating the 
                         mean standard deviation for samples within the different classes. 
                         The best classification accuracy with the different smoothing 
                         methods was the SG applied to the raw time series, with a global 
                         accuracy of 86% and kappa of 0.82.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59300",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM2PS",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSM2PS",
           targetfile = "59300.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "01 dez. 2020"
}


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