@InProceedings{MorandeiraFurt:2015:AsSuOb,
               author = "Morandeira, Natalia Soledad and Furtado, Luiz Felipe de Almeida",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Using polarimetric C-Band data to discriminate wetland vegetation 
                         in the Lower Paran{\'a} River floodplain: assesment of a 
                         supervised object-based Random Forests classifier",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6958--6965",
         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 = "The Lower Paran{\'a} River floodplain wetlands are dominated by 
                         herbaceous communities. Dominant macrophyte species have been 
                         classified in Plant Functional Types, summarizing their main 
                         structural and functional features and their expected responses to 
                         the environment. In a previous work, a polarimetric RADARSAT-2 
                         C-Band scene was classified with an unsupervised per-pixel 
                         approach on the coherence matrix (a progressive Wishart H/Alpha 
                         classifier), but a relatively low global accuracy (58.2%) and 
                         Kappa index (50.4%) were obtained. In this work, we assessed a 
                         supervised object-based Random Forests classifier on the same 
                         scene. Based in previous works in other areas, we expected a 
                         higher accuracy for the Random Forests classifier than for the 
                         Wishart one. However, we obtained a even lower global accuracy 
                         (55.2%) and Kappa index (40.6%). Also, most of the areas were 
                         assigned to Plant Functional Type A (corresponding to bulrush 
                         marshes). We compared the classifiers and discuss possible reasons 
                         for the lower accuracy of the object based classifier. Our results 
                         suggest that most of the errors can be caused by the high 
                         simmilarities between the Plant Functional Type classes and 
                         between short grasses and Bare Soil. Other possible explanation of 
                         the low accuracy of the Random Forests classifier is that it does 
                         not follow the statistical distribution of the polarimetric 
                         data.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1524",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4JGE",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4JGE",
           targetfile = "p1524.pdf",
                 type = "Sensoriamento remoto de microondas",
        urlaccessdate = "27 abr. 2024"
}