@InProceedings{NegriDutrSant:2012:StApMi,
               author = "Negri, Rog{\'e}rio Galante and Dutra, Luciano Vieira and 
                         Sant'Anna, Sidinei Jo{\~a}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Stochastic Approaches of Minimum Distance Method for Region Based 
                         Classification",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "al, Alvarez et",
                pages = "797--804",
         organization = "Progress in Pattern Recognition, Image Analysis, Computer Vision, 
                         and Applications;Iberoamerican Congress, 17. (CIARP).",
            publisher = "Springer-Verlag",
                 note = "{Lecture Notes in Computer Science} and {Volume 7441 2012}",
             keywords = "Computer vision, Image analysis, Image reconstruction, Remote 
                         sensing, Stochastic systems, Classification process, Image 
                         simulations, Minimum average distance, Minimum distance, 
                         Region-based, Simple approach, Simulation studies, Stochastic 
                         approach, stochastic distances.",
             abstract = "Normally remote sensing image classification is performed 
                         pixelwise which produces a noisy classification. One way of 
                         improving such results is dividing the classification process in 
                         two steps. First, uniform regions by some criterion are detected 
                         and afterwards each unlabeled region is assigned to class of the 
                         {"}nearest{"} class using a so-called stochastic distance. The 
                         statistics are estimated by taking in account all the reference 
                         pixels. Three variations are investigated. The first variation is 
                         to assign to the unlabeled region a class that has the minimum 
                         average distance between this region and each one of reference 
                         samples of that class. The second is to assign the class of the 
                         closest reference sample. The third is to assign the most frequent 
                         class of the k closest reference regions. A simulation study is 
                         done to assess the performances. The simulations suggested that 
                         the most robust and simple approach is the second variation.",
  conference-location = "Buenos Aires Berlin",
      conference-year = "2012",
                 isbn = "16113349 and {13: 9783642332746}",
                 issn = "03029743",
                label = "lattes: 8201805132981288 1 NegriDutrSant:2012:StApMi",
             language = "en",
           targetfile = "negri_stochastic.pdf",
                  url = "http://www.springerlink.com/content/kuv75681m5806613/",
               volume = "7441",
        urlaccessdate = "30 abr. 2024"
}