@InProceedings{NevesBendKörtFons:2015:CaStNo,
               author = "Neves, Alana K. and Bendini, Hugo do N. and K{\"o}rting, Thales 
                         S. and Fonseca, Leila M. G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and Divis{\~a}o de 
                         Processamento de Imagens (DPI), Instituto Nacional de Pesquisas 
                         Espaciais (INPE) and Divis{\~a}o de Processamento de Imagens 
                         (DPI), Instituto Nacional de Pesquisas Espaciais (INPE)",
                title = "\Combining time series features and data mining to detect 
                         \land cover patterns: a case study in northern Mato 
                         Grosso \",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Fileto, Renato and Korting, Thales Sehn",
                pages = "174--185",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 16. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "One product of the MODIS sensor (Moderate Resolution Imaging 
                         Spectroradiometer) is the EVI2 (Enhanced Vegetation Index). It 
                         generates images of around 23 observations each year, that 
                         combined can be interpreted as time series. This work presents the 
                         results of using two types of features obtained from EVI2 time 
                         series: basic and polar features. Such features were employed in 
                         automatic classification for land cover mapping, and we compared 
                         the influence of using single pixel versus object-based 
                         observations. The features were used to generate classification 
                         models using the Random Forest algorithm. Classes of interest 
                         included Agricultural Area, Pasture and Forest. Results achieved 
                         accuracies up to 91,70% for the northern region of Mato Grosso 
                         state, Brazil.",
  conference-location = "Campos do Jord{\~a}o",
      conference-year = "27 nov. a 02 dez. 2015",
                 issn = "2179-4820",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/3KP36L8",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3KP36L8",
           targetfile = "neves2015combining.pdf",
        urlaccessdate = "28 abr. 2024"
}