@InProceedings{AlmeidaJohRicNicRic:2017:CoMaCu,
               author = "Almeida, Luiz and Johann, Jerry Adriani and Richetti, Jonathan and 
                         Nicolau, Rafaela Fernandes and Richetti, Amanda Bordin",
                title = "Compara{\c{c}}{\~a}o no mapeamento da cultura de milho safrinha 
                         utilizando Machine Learning em imagens Landsat-8",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3455--3459",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The objective of this study was to compare the mapping of winter 
                         corn, using Machine Learning in Landsat-8 images in 2016 crop. For 
                         the images processing the software R 3.3.1 and ArcMap 10.0 were 
                         used. From a false-color RGB-564 composition of the Landsat-8 
                         images 5 classes of soil use and cover (urban area, water bodies, 
                         forest, winter corn and exposed soil) were polygonised. These 
                         sampled areas served as training data for the models. The Random 
                         Forest and the Gamboost classification methods were applied. To 
                         perform the accuracy of each mask random points were generated for 
                         each classification and a being point-to-point verification was 
                         performed. For the Gamboost method the value of the adjustment 
                         parameter that allowed the best result was 150 iterations (Mstop). 
                         While Random Forest presented the best classification result when 
                         the number of predictors sampled in each node (Mtry) was equal to 
                         2. The winter corn area identified in each model was about 
                         75,290.58 ha for GB and 57,220.29 ha for RF, with Global Accuracy 
                         of 87.75% and 79.0%, respectively. In spite of the differences 
                         between the classifiers used, both methods are effective in 
                         mapping the studied culture. Moreover, both methods presented 
                         great agility to classify and to obtain area, aiding in the 
                         ergonomics of the processes.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59282",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLSUS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLSUS",
           targetfile = "59282.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}