@InProceedings{GirolamoNetoFKSEBMT:2015:ClAuÁr,
               author = "Girolamo Neto, Cesare Di and Fonseca, Leila Maria Garcia and 
                         Korting, Thales Sehn and Sanches, Ieda Del Arco and Eberhardt, 
                         Isaque Daniel Rocha and Bendini, Hugo do Nascimento and Marujo, 
                         Rennan de Freitas Bezerra and Trabaquini, Kleber",
          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 {} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Classifica{\c{c}}{\~a}o autom{\'a}tica de {\'a}reas cafeeiras 
                         utilizando imagens de sensoriamento remoto e t{\'e}cnicas de 
                         minera{\c{c}}{\~a}o de dados",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1609--1616",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Coffee is the main crop produced in the southern of Minas Gerais 
                         state, Brazil, and techniques for estimating the area used for 
                         this crop are being intensely investigated in order to produce 
                         reliable yield estimates. Coffee trees have a similar spectral 
                         pattern to forest, making it difficult to automatically 
                         distinguish these land use types. This study evaluated the Random 
                         Forest and Decision Tree algorithms for an automatic 
                         classification of coffee areas in municipality of Machado, Minas 
                         Gerais, Brazil. First, the data were preprocessed by creating gray 
                         level masks in each of the 11 bands of a Landsat-8 image. Then the 
                         Random Forest and Decision Trees were trained and applied on the 
                         image in order to verify its potential for discriminating coffee 
                         areas. Lastly, the analysis and validation of the results were 
                         conducted using as reference one map manually classified. The 
                         Kappa index and the overall accuracy were used to assess the 
                         quality of the models tested. The Random Forest classifier 
                         presented better results than the Decision Trees, with an accuracy 
                         of 84.13% and a Kappa index of 0.6, which is more accurate when 
                         compared to previous studies. We also provide a list of bands that 
                         were not suitable for this type of classification.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "303",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM495M",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM495M",
           targetfile = "p0303.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}