@InProceedings{FariaFernFranFari:2017:InFoAm,
               author = "Faria, Maola Monique and Fernandes Filho, Elpidio In{\'a}cio and 
                         Francelino, M{\'a}rcio Rocha and Faria, Raiza Moniz",
                title = "Influ{\^e}ncia da forma de amostragem na exatid{\~a}o global e 
                         {\'{\i}}ndice kappa",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5976--5982",
         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 = "The procedure of classifying and grouping pixels of a digital 
                         image based on its spectral characteristics using algorithms in a 
                         computational program is called image classification. The 
                         objective of this article is to evaluate the effect of sampling in 
                         the form of polygons and points in global accuracy and in the 
                         kappa index in the classification of coffee areas in the Matas de 
                         Minas region of the state of Minas Gerais. In addition, the use of 
                         cross-validation and validation was evaluated using external data 
                         in the kappa index in the classification of coffee areas in the 
                         Matas de Minas region of the state of Minas Gerais. A cut of a 
                         Landsat 8 scene was used for the area of interest. On this scene, 
                         6,517 polygons were collected, with a mean of 12 pixels, 
                         distributed randomly throughout the study area. Based on the 
                         samples file in point format, the radiance values of each band of 
                         the Landsat 8 image were extracted. Four ways were defined in the 
                         definition of training samples of the Random Forest classifier. 
                         The procedures were performed using the software interface R and 
                         ArcGis 10.2. From the use of randomly collected points, they 
                         corroborate the accuracy, global accuracy and kappa, which are 
                         higher than those obtained by other treatments when using 
                         cross-validation, but the kappa obtained from the external 
                         validation is similar to the others.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59891",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMC3T",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMC3T",
           targetfile = "59891.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}