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@Article{GenovezJoneSantFrei:2019:OiSlCh,
               author = "Genovez, Patr{\'{\i}}cia Carneiro and Jones, Cathleen E. and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira and Freitas, Corina da 
                         Costa",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Jet 
                         Propulsion Laboratory (JPL), California Institute of Technology 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Oil slick characterization using a statistical region-based 
                         classifier applied to UAVSAR data",
              journal = "Journal of Marine Science and Engineering",
                 year = "2019",
               volume = "7",
               number = "2",
                pages = "e36",
                month = "Feb.",
             keywords = "oil slicks characterization, oil thickness, polarized SAR data, 
                         polarimetric SAR data (PolSAR), statistical region-based 
                         classification, uncertainty maps, UAVSAR.",
             abstract = "During emergency responses to oil spills on the sea surface, quick 
                         detection and characterization of an oil slick is essential. The 
                         use of Synthetic Aperture Radar (SAR) in general and polarimetric 
                         SAR (PolSAR) in particular to detect and discriminate mineral oils 
                         from look-alikes is known. However, research exploring its 
                         potential to detect oil slick characteristics, e.g., thickness 
                         variations, is relatively new. Here a Multi-Source Image 
                         Processing System capable of processing optical, SAR and PolSAR 
                         data with proper statistical models was tested for the first time 
                         for oil slick characterization. An oil seep detected by NASAs 
                         Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in 
                         the Gulf of Mexico was used as a study case. This classifier uses 
                         a supervised approach to compare stochastic distances between 
                         different statistical distributions (fx) and hypothesis tests to 
                         associate confidence levels to the classification results. The 
                         classifier was able to detect zoning regions within the slick with 
                         high global accuracies and low uncertainties. Two different 
                         classes, likely associated with the thicker and thinner oil 
                         layers, were recognized. The best results, statistically 
                         equivalent, were obtained using different data formats: 
                         polarimetric, intensity pair and intensity single-channel. The 
                         presence of oceanic features in the form of oceanic fronts and 
                         internal waves created convergence zones that defined the shape, 
                         spreading and concentration of the thickest layers of oil. The 
                         statistical classifier was able to detect the thicker oil layers 
                         accumulated along these features. Identification of the relative 
                         thickness of spilled oils can increase the oil recovery 
                         efficiency, allowing better positioning of barriers and skimmers 
                         over the thickest layers. Decision makers can use this information 
                         to guide aerial surveillance, in situ oil samples collection and 
                         clean-up operations in order to minimize environmental impacts.",
                  doi = "10.3390/jmse7020036",
                  url = "http://dx.doi.org/10.3390/jmse7020036",
                 issn = "2077-1312",
             language = "en",
           targetfile = "genovez_oil.pdf",
        urlaccessdate = "05 dez. 2020"
}


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