@InProceedings{BernardiDzedCarvAcer:2007:ClDiUs,
               author = "Bernardi, Henriqueta Veloso Ferreira and Dzedzej, Ma{\'{\i}}ra 
                         and Carvalho, Luis Marcelo Tavares de and Acerbi J{\'u}nior, 
                         Fausto Weimar",
          affiliation = "{Universidade Federal de Lavras - UFLA} and {Universidade Federal 
                         de Lavras - UFLA} and {Universidade Federal de Lavras - UFLA} and 
                         {Universidade Federal de Lavras - UFLA}",
                title = "Classifica{\c{c}}{\~a}o digital do uso do solo comparando os 
                         m{\'e}todos “pixel a pixel” e orientada ao objeto em imagem 
                         QuickBird",
            booktitle = "Anais...",
                 year = "2007",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares and Fonseca, Leila Maria Garcia",
                pages = "5595--5602",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 13. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "remote sensing, image processing, land-cover, high resolution 
                         image, sensoriamento remoto, processamento de imagens, cobertura 
                         do solo, imagem de alta resolu{\c{c}}{\~a}o.",
             abstract = "In remote sensing applications the increasing availability of new 
                         sensors, imaging in variety of ground scales, undoubtedly provides 
                         strong motivations for the experimentation of new classification 
                         technologies. This is the case for high spatial resolution images 
                         where the traditional pixel based classification methods is not 
                         able to capture the all variation in the image. This study 
                         compared the traditional pixel based classification, using the 
                         maximum likelihood algorithm, with an object-oriented 
                         classification, using the nearest neighbor algorithm. The results 
                         showed that the object-oriented classification yielded maps more 
                         accurate than the traditional pixel based classification.",
  conference-location = "Florian{\'o}polis",
      conference-year = "21-26 abr. 2007",
                 isbn = "978-85-17-00031-7",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2006/11.16.02.01.41",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2006/11.16.02.01.41",
           targetfile = "5595-5602.pdf",
                 type = "Processamento de Dados e de Imagens",
        urlaccessdate = "15 jun. 2024"
}