@InProceedings{UeharaCoQuKöDuRe:2019:ClAlCo,
               author = "Uehara, Tatiana Dias Tardelli and Correa, Sabrina Paes Leme Passos 
                         and Quevedo, Renata Pacheco and K{\"o}rting, Thales Sehn and 
                         Dutra, Luciano Vieira and Renn{\'o}, Camilo Daleles",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Classification algorithms comparison for landslide scars",
            booktitle = "Anais... do 20º Simp{\'o}sio Brasileiro de Geoinform{\'a}tica",
                 year = "2019",
               editor = "Lisboa Filho, Jugurta and Monteiro, Antonio Miguel Vieira",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 20. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "geoinformatica.",
             abstract = "Landslide inventory is an essential tool to support disaster risk 
                         mitigation. Using remote sensing images, it is usually obtained 
                         through pattern recognition. In this study, three classification 
                         methods are compared to detect landslides: Support Vector Machine 
                         (SVM), Artificial Neural Net (ANN) and Maximum Likelihood (ML). We 
                         used Sentinel-2A imagery, extracted and selected features for two 
                         areas in the Rolante River Catchment. The classification products 
                         showed that SVM classifier presented the best overall accuracy 
                         (OA) for Area 1 resulting in 87.143%; while for Area 2 ML showed 
                         the best OA equals to 86.831%.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos",
      conference-year = "11 -13 nov. 2019",
                 issn = "2179-4847",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGPDW34R/3UFE9MS",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFE9MS",
           targetfile = "158-169.pdf",
        urlaccessdate = "25 abr. 2024"
}