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@InProceedings{SáGaMuQuViRo:2019:DeSaUs,
               author = "S{\'a}, Jos{\'e} Alberto Silva de and Gama, F{\'a}bio Furlan 
                         and Mura, Jos{\'e} Cl{\'a}udio and Queiroz, Gilberto Ribeiro de 
                         and Vinhas, L{\'u}bia and Rocha, Br{\'{\i}}gida Ramati Pereira 
                         da",
          affiliation = "{} and {} and {} and {} and {} and {Universidade Federal do 
                         Par{\'a} (UFPA)}",
                title = "Detection of sandbanks using synthetic aperture radar images time 
                         series",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "3303--3306",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Synthetic aperture radar, time series, sandbank, Amazon region.",
             abstract = "A sandbank consists of the accumulation of sediments (sand and 
                         gravel) deposited in a riverbed or along the coast, constituting 
                         an obstacle to navigation. This work aimed to develop a technique 
                         for the detection of sandbanks located in an Amazonian estuarine 
                         region (Guajar{\'a}s bay) using synthetic aperture radar images 
                         time series in order to assist the obstacles monitoring in the 
                         local navigation. We used 28 images of the Sentinel-1 Mission; 
                         Orbital Platform: S1A; Band: C; Product Type: Ground Range 
                         Detected (GRDH); Polarizations: VV and VH; Sensor Mode: 
                         Interferometric Wide Swath Mode (IW). The results showed a 
                         significant stratification for 3 classes (Water, Sandbank and 
                         River Island), with accuracy equal to 91,1% for an automatic 
                         classification by the k-NN algorithm.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3TUEM25",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3TUEM25",
           targetfile = "97211.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "22 jan. 2021"
}


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