@InProceedings{SanchezPASSCBC:2019:LaCoCl,
               author = "Sanchez, Alber and Picoli, Michelle and Andrade, Pedro Ribeiro de 
                         and Sim{\~o}es, Rolf and Santos, Lorena and Chaves, Michel and 
                         Begotti, Rodrigo and Camara, Gilberto",
          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)} and 
                         {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Land cover classifications of clear-cut deforestation using deep 
                         learning",
            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 = "Using Deep Learning Neural Networks, we made supervised 
                         classifications of a small region of the Brazilian Amazon in order 
                         to map clearcut deforestation. We organized Landsat 8 Surface 
                         Reflectance images into time series and we classify the images 
                         using the bands ad a Linear Mixture Model. We obtained similar 
                         accuracies using both data sets when compared to the data reported 
                         by the Brazilian Amazon Deforestation Monitoring Program (PRODES). 
                         These results suggest the possibilities of using automatic 
                         supervised techniques to extend the coverage of forest monitoring 
                         programs to those excluded areas by lack of human resources.",
  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/3UFDE4P",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFDE4P",
           targetfile = "48-56.pdf",
        urlaccessdate = "23 abr. 2024"
}