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@Article{NegriFreSilMenDut:2018:ReClPo,
               author = "Negri, Rog{\'e}rio Galante and Frery, Alejandro C. and Silva, 
                         Wagner B. and Mendes, Tatiana S. G. and Dutra, Luciano Vieira",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade Federal 
                         de Alagoas (UFAL)} and {Instituto Militar de Engenharia (IME)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)}",
                title = "Region-based classification of PolSAR data using radial basis 
                         kernel functions with stochastic distances",
              journal = "International Journal of Digital Earth",
                 year = "2018",
               volume = "2018",
             keywords = "PolSAR, image classification, stochastic distance, minimum 
                         distance classifier, SVM.",
             abstract = "Region-based classification of PolSAR data can be effectively 
                         performed by seeking for the assignment that minimizes a distance 
                         between prototypes and segments. Silva et al. [Classification of 
                         segments in PolSAR imagery by minimum stochastic distances between 
                         wishart distributions. IEEE Journal of Selected Topics in Applied 
                         Earth Observations and Remote Sensing 6 (3): 12631273] used 
                         stochastic distances between complex multivariate Wishart models 
                         which, differently from other measures, are computationally 
                         tractable. In this work we assess the robustness of such approach 
                         with respect to errors in the training stage, and propose an 
                         extension that alleviates such problems. We introduce robustness 
                         in the process by incorporating a combination of radial basis 
                         kernel functions and stochastic distances with Support Vector 
                         Machines (SVM). We consider several stochastic distances between 
                         Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, R{\'e}nyi, 
                         and Hellinger. We perform two case studies with PolSAR images, 
                         both simulated and from actual sensors, and different 
                         classification scenarios to compare the performance of Minimum 
                         Distance and SVM classification frameworks. With this, we model 
                         the situation of imperfect training samples. We show that SVM with 
                         the proposed kernel functions achieves better performance with 
                         respect to Minimum Distance, at the expense of more computational 
                         resources and the need of parameter tuning. Code and data are 
                         provided for reproducibility.",
                  doi = "10.1080/17538947.2018.1474958",
                  url = "http://dx.doi.org/10.1080/17538947.2018.1474958",
                 issn = "1753-8947",
             language = "en",
           targetfile = "negri_region.pdf",
        urlaccessdate = "25 nov. 2020"
}


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