@InProceedings{MedeirosCastErthDutr:2011:ClImMé,
               author = "Medeiros, Ivo Paix{\~a}o de and Castro Filho, Carlos Alberto 
                         Pires de and Erthal, Guaraci Jos{\'e} and Dutra, Luciano Vieira",
          affiliation = "{Instituto Tecnol{\'o}gico da Aeron{\'a}utica - ITA} and 
                         {Instituto Nacional de Pesquisas Espaciais - INPE} and {Instituto 
                         Nacional de Pesquisas Espaciais - INPE} and {Instituto Nacional de 
                         Pesquisas Espaciais - INPE}",
                title = "Classifica{\c{c}}{\~a}o de imagens pelo m{\'e}todo de 
                         {\'A}rvore de Decis{\~a}o Obl{\'{\i}}qua",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "4255--4262",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "pattern recognition, classification, decision tree, reconhecimento 
                         de padr{\~o}es.",
             abstract = "The Oblique Decision Tree (ODT) classification method has the 
                         advantage of dividing feature spaces using multidimensional 
                         hyperplanes that are oblique to the Cartesian axes. Because it is 
                         a non-parametric classification method, it also is capable of 
                         classifying images containing different statistical distribution. 
                         This paper aims to present a model of an ODT and perform tests 
                         comparing its classification results with other traditional 
                         classification methods. The ODT developed is binary and uses the 
                         Exchange Method for splitting each node into two subsets of 
                         classes, along with Fisher´s Linear Discriminant to calculate the 
                         parameters for the hyperplanes. Tests have been done using 
                         Polarimetric Interferometric and Synthetic Aperture Radar data 
                         from the brazilian Terrestrial Cartography Subproject, also known 
                         as Amazon Radiography, of the Geographic Service of Brazilian Army 
                         (DSG). Throughout the tests the ODT showed slightly better 
                         classification results compared to the Ordinary Binary 
                         Classification Tree, obtaining better overall accuracy (86.06%) 
                         and smaller tree. Besides that, the ODT showed better results than 
                         those obtained with traditional classifiers such as the Maximum 
                         Likelihood (85.44%), Nearest Neighbor (77.07%) and Mahalanobis 
                         Distance (78.72%). In the other hand, the Support Vector Machine 
                         classification method obtained higher overall accuracy (87.37%) 
                         although a much higher processing time is needed.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/39UFKJE",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/39UFKJE",
           targetfile = "p0953.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "15 jun. 2024"
}