@Article{GenovezFreSanBenLor:2017:OiSlDe,
               author = "Genovez, Patr{\'{\i}}cia 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 {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Centro de Pesquisa da 
                         Petrobr{\'a}s (CENPES)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Oil Slicks Detection From Polarimetric data using stochastic 
                         distances between complex wishart distributions",
              journal = "IEEE Journal of Selected Topics in Applied Earth Observations and 
                         Remote Sensing",
                 year = "2017",
               volume = "10",
               number = "2",
                pages = "463--477",
                month = "Feb.",
             keywords = "Information theory, oil slicks detection, polarimetry, 
                         region-based classification, stochastic distances, synthetic 
                         aperture radar (SAR), uncertainty maps.",
             abstract = "Polarimetric synthetic aperture radars (PolSAR) have been used to 
                         detect oil slicks at the sea surface. Different techniques to 
                         extract information from polarimetric data, using an adequate 
                         statistical distribution are currently available. A region-based 
                         classifier for PolSAR data - named PolClass - uses a supervised 
                         approach to compare stochastic distances between scaled complex 
                         Wishart distributions and hypothesis tests to associate confidence 
                         levels into the classification results. In this paper, the 
                         integrated use of these distances together with the uncertainty 
                         maps is applied for the first time to detect oil slicks. A 
                         quad-pol Radarsat-2 data, acquired during one open-water 
                         controlled exercise, was used to perform this test. The PolClass 
                         achieved similar overall accuracies for four stochastic distances, 
                         reaching 96.54% of global accuracy, the best result obtained by 
                         the Hellinger distance. A comparison between the full-and dual-pol 
                         matrices indicated that the results obtained with the VV-HH-HV, 
                         HH-HV, and VV-HV polarizations are statistically equivalent, but 
                         different from that obtained using the HH-VV. Therefore, the 
                         exclusion of the HV channel affected the detection of only mineral 
                         oils. The classifier demonstrated the 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. The proposed approach will 
                         contribute to the understanding of where different physical and 
                         chemical processes may be acting, associating confidence levels to 
                         the classification results.",
                  doi = "10.1109/JSTARS.2016.2628325",
                  url = "http://dx.doi.org/10.1109/JSTARS.2016.2628325",
                 issn = "1939-1404 and 2151-1535",
             language = "en",
           targetfile = "genovez_oil.pdf",
        urlaccessdate = "19 abr. 2024"
}