@InProceedings{OliveiraZeilSant:2007:EsCaCu,
               author = "Oliveira, Ivani Matos de and Zeilhofer, Peter and Santos, Emerson 
                         Soares dos",
          affiliation = "{Universidade Federal do Mato Grosso (UFMT). ICET. F{\'{\i}}sica 
                         e Meio Ambiente.} and {Universidade Federal do Mato Grosso (UFMT). 
                         ICHS. Departamento de Geografia.} and {Universidade Federal do 
                         Mato Grosso (UFMT). ICHS. Departamento de Geografia.}",
                title = "Segmenta{\c{c}}{\~a}o para classifica{\c{c}}{\~a}o de 
                         {\'a}reas urbanas a partir de imagem digital do Landsat7/ETM+: 
                         estudo de caso – Cuiab{\'a} - MT",
            booktitle = "Anais...",
                 year = "2007",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares and Fonseca, Leila Maria Garcia",
                pages = "6011--6018",
         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 = "Landsat 7, classification, segmentation, urban environment, 
                         classifica{\c{c}}{\~a}o, segmenta{\c{c}}{\~a}o.",
             abstract = "A case study for a supervised classification of multispectral 
                         Landsat ETM imagery from the urban area of Cuiab{\'a} / 
                         V{\'a}rzea Grande is presented. Two classification techniques 
                         implemented in the SPRING software were compared: Maximum 
                         Likelihood and Bhattacharrya with previous segmentation by region 
                         growing. Overall classification accuracies of about 61 and 55 % 
                         indicate the limitations of mid resolution imagery for land use 
                         mapping in urban areas. Previous segmentation (Bhattacharrya) 
                         improve classification accuracies substantially.",
  conference-location = "Florian{\'o}polis",
      conference-year = "21-26 abr. 2007",
                 isbn = "978-85-17-00031-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2006/11.15.20.21",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2006/11.15.20.21",
           targetfile = "6011-6018.pdf",
                 type = "Processamento de Dados e de Imagens",
        urlaccessdate = "15 jun. 2024"
}