@InProceedings{GassGalaVarg:2017:CoAlKM,
               author = "Gass, Sidnei Lu{\'{\i}}s Bohn and Galafassi, Cristiano and 
                         Vargas, Rog{\'e}rio Rodrigues de",
                title = "Comparativo entre os algoritmos K-Means e ckMeans para mapeamento 
                         automatizado de uso do solo",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6376--6382",
         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 = "Remote sensing allow us to acquire information about an object or 
                         phenomenon without the need to make physical contact with the 
                         object, which turn it usable in many fields, like hydrology, 
                         ecology, oceanography, glaciology, geology. Remote Sensing 
                         generally refers to the use of satellite-based (or aircraft) 
                         sensor technologies to detect and classify objects on Earth. 
                         Classification is the process of extracting information in images 
                         (or data) to recognize patterns and homogeneous objects and are 
                         used in remote sensing to map areas of the earth''s surface. This 
                         article makes a comparison between two algorithms used in image 
                         classification applied to remote sensing. The first one is the 
                         well-known K-Means, that has the characteristic to be fast and its 
                         modeling is relatively simple, and the second is the fuzzy ckMeans 
                         algorithm that allows to model inaccurate data according to their 
                         membership degree. The ckMeans algorithm proved to be a good 
                         alternative in the image segmentation process. To validate the 
                         work we compared the classification of an image, obtained by a 
                         satellite, of the western border of the state of Rio Grande do Sul 
                         and defined a priori four clusters. Then, the classification 
                         between K-Means and ckMeans algorithms was performed. Finally, a 
                         domain knowledge specialist discussed the resultant classification 
                         obtained by these algorithms.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59893",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMCQ7",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMCQ7",
           targetfile = "59893.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}