@InProceedings{SouzaRodr:2017:ReMaFe,
               author = "Souza, Simmon Viegas and Rodrigues, Suzan Waleska Pequeno",
                title = "Reconhecimento e mapeamento das fei{\c{c}}{\~o}es fluviais da 
                         ?Ilha Grande do Tapar{\'a}? (Santar{\'e}m-PA) a partir do 
                         processamento de imagens RapidEye e SAR-SIPAM",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4843--4850",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The objective of this paper was to recognize and map the main 
                         fluvial environments along the Amazon River from remote sensing 
                         data. Optical images of the REIS sensor were used aboard the 
                         RapidEye satellite, with the goal of mapping the region in detail, 
                         and SAR-SIPAM synthetic aperture radar images, which allow us to 
                         visualize the region of study with reduced atmospheric 
                         interference. In order to complement these data, the integration 
                         of the radar and optical data was made providing the SPC-SAR, 
                         product of this synergism. The mapping of the environments was 
                         based on the non-supervised classification of the REIS, SAR and 
                         SPC-SAR images, which allowed the identification of lake classes 
                         with swamp, canal, fluvial delta, river terrace, river beach and 
                         water. The generated classes were and their features were 
                         associated to the environments of the study area. The general 
                         precisions of the classifications were 93.33% and 0.90 for the 
                         REIS sensor; 93.33% and 0.89 for SAR; and 90.66% and 0.86 for the 
                         SPC-SAR product. These results indicate that both data provide us 
                         with important and accurate information about the study surface 
                         and that the integrated product can complement these data allowing 
                         a more accurate mapping of the fluvial environments in the Amazon 
                         region.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60054",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM3Q2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM3Q2",
           targetfile = "60054.pdf",
                 type = "Geomorfologia",
        urlaccessdate = "27 abr. 2024"
}