@InProceedings{Negri:2017:DeNoFu,
               author = "Negri, Rog{\'e}rio Galante",
                title = "Desenvolvimento de Novas Fun{\c{c}}{\~o}es Kernel para 
                         Classifica{\c{c}}{\~a}o Contextual de Imagens",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2293--2300",
         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 = "Kernel functions have revolutionized the theory and practice in 
                         Pattern Recognition, and consequently the image classification 
                         applications. Besides allows the definition of non-linear versions 
                         of methods like Support Vector Machine (SVM), such functions allow 
                         generalize the application of these methods on the classification 
                         of non-vector patterns, such as probability distributions, 
                         information sets, etc. This possibility motivates the development 
                         of kernel functions able to deal with the context which the pixels 
                         are inserted and consequently inducing contextual classifications 
                         when adopted. In this initial study, three kernel functions are 
                         proposed for contextual classification. These functions are based 
                         on stochastic distances, non-parametric statistical tests and 
                         spatial variation modeling. A case study about the land use and 
                         land cover classification with an ALOS PALSAR image is carried in 
                         order to compare the performance of the SVM method though the use 
                         of the developed kernel functions. Comparisons with other 
                         contextual methods based on SVM are included in this analyzes. The 
                         results shows potential on the new proposals, especially the 
                         kernels based on stochastic distance and nonparametric statistical 
                         test.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "61623",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLQAU",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLQAU",
           targetfile = "61623.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}