@InProceedings{CoelhoVMADGBIV:2017:MéClOr,
               author = "Coelho, Guilherme Leite Nunes and Volpato, Margarete Marin Lordelo 
                         and Maciel, Daniel Andrade and Alves, Helena Maria Ramos and 
                         Dantas, Mayara Fontes and Gon{\c{c}}alves, Thais Gabriela and 
                         Barata, Rafael Alexandre Pena and Inacio, Franklin Daniel and 
                         Vieira, Tatiana Grossi Chquiloff and {Juli{'a}n}",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "M{\'e}todos de classifica{\c{c}}{\~a}o orientada ao objeto 
                         utilizando imagens Sentinel-2",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1739--1746",
         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 aim of this study was to evaluate the performance of 
                         classifiers support vectors machine (SVM) and K-nearest neighbor 
                         (K-NN) for object-based image analysis using Sentinel-2 images. 
                         Tr{\^e}s Pontas city in the southern region of Minas Gerais was 
                         used as a study area. Sentinel-2 image with a spatial resolution 
                         of 10 meters was obtained by merging and resampling 10 of all 13 
                         bands. Based on prior knowledge of the landscape were defined 5 
                         classes of use and land cover. The step of image processing 
                         occurred in ENVI 5.0. In segmentation step was applied to 10 
                         values of segment settings that uses the algorithm edge and 60 for 
                         merge setting using the algorithm full lambda schedule, 
                         respectively and targeting settings and unity. After that was 
                         collected train samples of all 5 predefine class. The 
                         classification was performed by SVM and K-NN algorithms. Both 
                         obtained satisfactory results in evaluation of accuracy with Kappa 
                         values for the SVM of 0.87 and 0.85 for K-NN. The results show 
                         that the object-based image analysis using Sentinel-2 images are 
                         robust and satisfying. The method allowed the correct 
                         classification of different vegetation types found in landscape. 
                         Furthermore, is recommended for preparation maps of land use and 
                         land cover that may assist the territorial planning.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59613",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLP8H",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLP8H",
           targetfile = "59613.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}