@InProceedings{GenovezFreSanBenLor:2017:OiSlCl,
               author = "Genovez, Patricia Carneiro and Freitas, Corina da Costa and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira and Bentz, Cristina Maria and 
                         Lorenzzetti, Jo{\~a}o Ant{\^o}nio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and {} 
                         and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Oil Slicks Classification using Multivariate Statistical Modelling 
                         Applied to SAR and PolSAR Data",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6764--6771",
         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 = "Polarimetric Synthetic Aperture Radars (PolSAR) have been used to 
                         detect oil slicks at sea surface. The numerous platforms 
                         available, acquiring data in different formats and configurations, 
                         pose as a challenge to understand which is the better format and 
                         statistical modeling to improve the oil detection. To contribute 
                         with this issue, a combination of different data formats in single 
                         look complex, intensity and amplitude, with full and dual 
                         polarimetric channels, were evaluated considering adequate 
                         statistical modeling to classify each data type. The better 
                         results were obtained by the full and dual-pol matrices, however 
                         when the HV channel is excluded the accuracy levels are damaged. 
                         Therefore, it is better use the data in intensity or amplitude 
                         preserving the HV channel, than use a polarimetric data without 
                         this channel. The classifier demonstrated potential to detect the 
                         three types of oils released, being more effective in detecting 
                         biogenic oils rather than mineral oils. The uncertainty levels 
                         increase from the center to the border of the mineral oil slicks, 
                         indicating the presence of transition regions, possibly related to 
                         different weathering mechanisms. Future studies should be done 
                         including more SAR images, with known occurrences and field data 
                         to investigate properly the trade-offs related with each data 
                         format to discriminate different oil types.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59356",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDG8",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDG8",
           targetfile = "59356.pdf",
                 type = "Sensoriamento remoto de microondas",
        urlaccessdate = "26 abr. 2024"
}