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@Article{GenovezEbeFreBenFre:2017:InHySy,
               author = "Genovez, Patr{\'{\i}}cia Carneiro and Ebecken, Nelson and 
                         Freitas, Corina da Costa and Bentz, Cristina Maria and Freitas, 
                         Ramon",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Centro de Pesquisa da 
                         Petrobr{\'a}s (CENPES)} and {Camargo-Schubert Wind Engineering}",
                title = "Intelligent hybrid system for dark spot detection using SAR data",
              journal = "Expert Systems With Applications",
                 year = "2017",
               volume = "81",
                pages = "384--397",
                month = "Sept.",
             keywords = "Cluster analysis, Computational Intelligence, Digital Image 
                         Processing, Feature selection, Oil spills detection, Synthetic 
                         Aperture Radar.",
             abstract = "Synthetic Aperture Radars (SAR) are the main instrument used to 
                         support oil detection systems. In the microwave spectrum, oil 
                         slicks are identified as dark spots, regions with low backscatter 
                         at sea surface. Automatic and semi-automatic systems were 
                         developed to minimize processing time, the occurrence of false 
                         alarms and the subjectivity of human interpretation. This study 
                         presents an intelligent hybrid system, which integrates automatic 
                         and semi-automatic procedures to detect dark spots, in six steps: 
                         (I) SAR pre-processing; (II) Image segmentation; (III) Feature 
                         extraction and selection; (IV) Automatic clustering analysis; (V) 
                         Decision rules and, if needed; (VI) Semi-automatic processing. The 
                         results proved that the feature selection is essential to improve 
                         the detection capability, keeping only five pattern features to 
                         automate the clustering procedure. The semi-automatic method gave 
                         back more accurate geometries. The automatic approach erred more 
                         including regions, increasing the dark spots area, while the 
                         semi-automatic method erred more excluding regions. For 
                         well-defined and contrasted dark spots, the performance of the 
                         automatic and the semi-automatic methods is equivalent. However, 
                         the fully automatic method did not provide acceptable geometries 
                         in all cases. For these cases, the intelligent hybrid system was 
                         validated, integrating the semi-automatic approach, using compact 
                         and simple decision rules to request human intervention when 
                         needed. This approach allows for the combining of benefits from 
                         each approach, ensuring the quality of the classification when 
                         fully automatic procedures are not satisfactory.",
                  doi = "10.1016/j.eswa.2017.03.037",
                  url = "http://dx.doi.org/10.1016/j.eswa.2017.03.037",
                 issn = "0957-4174",
             language = "en",
           targetfile = "genovez_intelligent.pdf",
        urlaccessdate = "29 nov. 2020"
}


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