@InProceedings{RichettiSiBePaCoJo:2019:MaLeAl,
               author = "Richetti, Jonathan and Silva, La{\'{\i}}za Cavalcante de 
                         Albuquerque and Becker, Willyan Ronaldo and Paludo, Alex and 
                         Comineti, Humberto Jo{\~a}o and Johann, Jerry Adriani",
          affiliation = "{Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)} and 
                         {Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)} and 
                         {Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)} and 
                         {Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)} and 
                         {Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)} and 
                         {Universidade Estadual do Oeste do Paran{\'a} (UNIOESTE)}",
                title = "Machine learning algorithms to land cover mapping with Landsat-8",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "2061--2064",
         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 = "data mining, classification, remote sensing, satellite image.",
             abstract = "Data mining algorithms applied to satellite image can be used to 
                         land cover mapping. This brings agility to the process of mapping 
                         areas and the accuracy can be assessed. However, with many machine 
                         learning algorithms it is hard to assess the best one for a giving 
                         task. Therefore, this work aims to test different machine learning 
                         algorithms to classify land cover using high-resolution imagery. 
                         Four algorithms were tested: Bagged CART, Random Forest (RF), 
                         Neural Network, and Model Averaged Neural Network in the Landsat-8 
                         tile path/row 223/078 from December 13, 2017. A sample of 42,676 
                         pixels in eight different categories (city, exposed soil, soybean, 
                         corn, turnip, pasture, forest, and water) was used. From all 
                         pixels, 25,607 pixels (60%) were used as training set and 17,069 
                         pixels (40%) were used as testing set. The results shown that RF 
                         algorithm performed better with overall accuracy of 97% and kappa 
                         of 0.946.",
  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/3TUT4Q5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3TUT4Q5",
           targetfile = "97284.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "09 maio 2024"
}