@InProceedings{MendesPoz:2011:ClImAé,
               author = "Mendes, Tatiana Sussel Gon{\c{c}}alves and Poz, Aluir 
                         Porf{\'{\i}}rio Dal",
          affiliation = "{Universidade Estadual Paulista – FCT/UNESP} and {Universidade 
                         Estadual Paulista – FCT/UNESP}",
                title = "Classifica{\c{c}}{\~a}o de imagens a{\'e}reas de 
                         alta-resolu{\c{c}}{\~a}o utilizando Redes Neurais Artificiais e 
                         dados de varredura a laser",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7792--7799",
         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 = "street detection, Artificial Neural Networks, normalized Digital 
                         Surface Model, detec{\c{c}}{\~a}o de vias, Redes Neurais 
                         Artificiais, Modelo Digital de Superf{\'{\i}}cie normalizado.",
             abstract = "The problem of urban road network extraction from digital image 
                         can be simplified by detecting RoI (Region of Interest) 
                         corresponding to streets using image classification procedure. The 
                         use of only radiometric data in image classification process can 
                         result in overlapping classes, due to objects that have similar 
                         spectral characteristics. The use of additional information (e.g. 
                         laser scanner data) can contribute to the distinction between 
                         these objects. In order to isolate regions corresponding to 
                         streets in urban environments, the goal of this work is to 
                         evaluate the result of the classification by Artificial Neural 
                         Networks, using two data sets. The first one uses only the RGB 
                         high-resolution aerial images and the second one combines RGB 
                         images with an image representing the aboveground objects, which 
                         was obtained from the laser scanner altimetry data. The analysis 
                         of the results showed that the latter data set allows better 
                         results, as it reduces the confusion between classes representing 
                         mainly streets and building roofs.",
  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/39UFL72",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/39UFL72",
           targetfile = "p0654.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "03 maio 2024"
}