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@InProceedings{SotheAlScLiLiMiTo:2019:SiSpMa,
               author = "Sothe, Camile and Almeida, Cl{\'a}udia Maria de and Schimalski, 
                         Marcos Benedito and Liesenberg, Veraldo and Lima, Carla Luciane 
                         and Miyoshi, Gabriela Takahashi and Tommaselli, Antonio Maria 
                         Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade do 
                         Estado de Santa Catarina (UDESC)} and {Universidade do Estado de 
                         Santa Catarina (UDESC)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual Paulista (UNESP)} and 
                         {Universidade Estadual Paulista (UNESP)}",
                title = "Single-tree species mapping using one-class classification methods 
                         and UAV hyperspectral images",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "343--346",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "endangered tree species, Support Vector Machine, Random Forest, 
                         Principal Component Analysis, Minimum Noise Fraction.",
             abstract = "Progress in tree species mapping with hyperspectral data usually 
                         is limited by the multi-class classification framework, which 
                         imposes the requirement of exhaustively defining all species 
                         encountered in a landscape. As the research objective may be to 
                         map only one or a few species of interest, it is necessary to 
                         explore alternative classification methods that may be used to 
                         more efficiently detect a single species. In this study, we used 
                         UAV hyperspectral data to detect one endangered tree species, 
                         Araucaria angustifolia, in a subtropical forest area comparing the 
                         performance of two one-class classifiers (OCC): OCSVM and OCRF. 
                         Besides the 25 spectral bands (SB), we also tested two other 
                         datasets: one comprising the first five MNF components, and the 
                         other one comprising the first five PCA. Both algorithms and all 
                         the datasets reached good results, with F-score varying from 0.81 
                         for OCRF and SB dataset, to 1 for OCSVM associated with the PCA 
                         dataset.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3UAM9T8",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3UAM9T8",
           targetfile = "97927.pdf",
                 type = "Sensoriamento remoto hiperespectral",
        urlaccessdate = "21 jan. 2021"
}


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