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@InProceedings{DutraNegrSantLu:2015:CaStNe,
               author = "Dutra, Luciano Vieira and Negri, Rog{\'e}rio Galante and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira and Lu, Dengsheng",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Development of dissimilarity functions using stochastic distances 
                         for region-based land cover classification: a case study near 
                         Tapaj{\'o}s Flona, Par{\'a} state, Brazil",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1655--1662",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "One recent alternative to standard pixel based classification of 
                         remote sensing data, is the region based classification (RBC), 
                         which has been proved particularly useful when analyzing high 
                         resolution imagery of complex environments, like urban areas. 
                         First the imagery is decomposed into homogenous regions, following 
                         some criteria, and then each region is classified to one of the 
                         classes of interest. Normally, classification is performed by 
                         using stochastic distances, which measures the distance of the 
                         pixels distribution inside an unknown region and the 
                         representative distributions of each class. The class, whose 
                         distance is minimum to the unknown region distribution, is 
                         assigned to the region, which is known as stochastic minimum 
                         distance classification (SMDC). A problem appears when one, or 
                         more, class distribution is multi-modal, which violates the 
                         Gaussian hypotheses used for classes distributions, degrading the 
                         mapping accuracy. This investigation reports the usage of 
                         different compositions of the original stochastic minimum distance 
                         classifier with the objective of getting less sensitive results 
                         for classification, when potentially multi-modal classes are used. 
                         The newly developed classifier, called stochastic nearest distance 
                         classifier (SNDC), produced the best result when compared with the 
                         original classifier and other possible compositions, in a study 
                         case near the Tapaj{\'o}s Flona, in Par{\'a} state, Brazil. This 
                         study also brings, as methodological contribution, a criterion to 
                         improve the segmentation phase of RBC methods.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "309",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4973",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4973",
           targetfile = "p0309.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "04 dez. 2020"
}


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