@InProceedings{WatanabeImaiSamiRoch:2009:EsCaPl,
               author = "Watanabe, Fernanda Sayuri Yoshino and Imai, Nilton Nobuhiro and 
                         Samizava, Tiago Matsuo and Rocha, Paulo Cesar",
          affiliation = "{Universiade Estadual Paulista - Faculdade de Ci{\^e}ncias e 
                         Tecnologia/SP} and {Universiade Estadual Paulista - Faculdade de 
                         Ci{\^e}ncias e Tecnologia/SP} and {Universiade Estadual Paulista 
                         - Faculdade de Ci{\^e}ncias e Tecnologia/SP} and {Universiade 
                         Estadual Paulista - Faculdade de Ci{\^e}ncias e Tecnologia/SP}",
                title = "Classifica{\c{c}}{\~a}o da vegeta{\c{c}}{\~a}o de {\'a}reas 
                         {\'u}midas baseada em redes neurais artificiais: estudo de caso 
                         da plan{\'{\i}}cie fluvial do alto rio Paran{\'a}",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "5515--5522",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "CBERS image, artificial neural network, remote sensing, 
                         floodplain, redes neurais artificiais, sensoriamento remoto, 
                         plan{\'{\i}}cie de inunda{\c{c}}{\~a}o.",
             abstract = "This work attempts to contribute to the understanding of the 
                         distribution of the vegetation covering on floodplain Paran{\'a} 
                         River. Those mappings were accomplished by the multi-source data, 
                         through the multispectral images of CCD-CBERS, integrated with 
                         topography data, texture and vegetation index (NDVI) extracted 
                         from multispectral images and the DEM (Digital Elevation Model) 
                         from SRTM mission (Shuttle Radar Topography Mission). There was 
                         geometric correction of the multispectral images and DEM, using 
                         the Mosaic Geocover 2000 as reference. The atmospheric correction 
                         of multispectral images was done through the model of reflectance 
                         5S, available in Scoradis software, using atmospheric data gotten 
                         from the MODIS sensor, TERRA platform. The adopted approach was 
                         based on the artificial neural network supervised classification 
                         with the Backpropagation algorithm, available in the Idrisi 
                         software. It is hoped that this work can contribute to the 
                         understanding of the distribution of vegetation covering lands of 
                         floodplains. The classification will be refined through the 
                         information extracted from multispectral images taken with 
                         airborne cameras of high spatial resolution.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.17.20.30",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.17.20.30",
           targetfile = "5515-5522.pdf",
                 type = "Monitoramento e Modelagem Ambiental",
        urlaccessdate = "20 abr. 2024"
}