@InProceedings{CarvalhoFerKörAraAnd:2019:RaFoSu,
               author = "Carvalho, Nath{\'a}lia Silva de and Ferreira, Igor Jos{\'e} 
                         Malfetoni and K{\"o}rting, Thales Sehn and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Anderson, Liana Oighenstein",
          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 {Centro Nacional de Monitoramento e Alertas 
                         de Desastres Naturais (CEMADEN)}",
                title = "Random forest and support vector machine applied for mapping 
                         burned areas in Amazon",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "2833--2836",
         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 = "Pattern recognition, geobia, fire, degradation.",
             abstract = "The use of fire for land management is one of the main anthropic 
                         activities that have led to the impoverishment of tropical 
                         forests. Therefore, mapping these areas is paramount for public 
                         policies implementation. Currently, machine learning techniques 
                         have shown very effective results in the classification of land 
                         cover on extensive areas. This paper aims to compare the Random 
                         Forest (RF) and Support Vector Machine (SVM) algorithms 
                         performance on burned areas mapping in Amazon. Using a 
                         multiresolution segmentation algorithm applied to a Landsat image, 
                         the training dataset included 300 objects of burned and nonburned 
                         areas. Additionally, 24 attributes were tested in both RF and SVM 
                         approaches. An overall classification accuracy of 91% was achieved 
                         by RF and SVM models using spectral and geometric attributes. 
                         Nonetheless, regarding the omissions and inclusion errors, SVM 
                         models had the best performance on burned areas mapping.",
  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/3U9MCQP",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U9MCQP",
           targetfile = "97823.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "28 abr. 2024"
}