@InProceedings{CamargoSanoAlmeMura:2019:DaMiTe,
               author = "Camargo, Fl{\'a}vio Fortes and Sano, Edson Eyji and Almeida, 
                         Cl{\'a}udia Maria de and Mura, Jos{\'e} Cl{\'a}udio",
          affiliation = "{Departamento Nacional de Infraestrutura de Transporte (DNIT)} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Data mining techniques applied to ALOS-2/PALSAR-2 satellite 
                         imagery for land use and land cover classification",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "399--402",
         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 = "Machine learning, Weka, decision tree, random forest, multilayer 
                         perceptron.",
             abstract = "This paper proposes a workflow for the classification of synthetic 
                         aperture radar (SAR) images obtained by the ALOS-2/PALSAR-2 
                         satellite, aiming at the land use and land cover mapping. The 
                         study area is located in the western portion of Federal District 
                         of Brazil. The presented approach combines multiresolution 
                         segmentation, object attributes, and iterative machine learning 
                         procedures. A set of 397 attributes was generated based on the 
                         amplitude images, HH and HV polarizations. These attributes were 
                         processed in the WEKA 3.8 software using the J48 decision tree, 
                         Random Forest and Multilayer Perceptron Artificial Neural Network 
                         classifiers. Classification results attained Kappa indices higher 
                         than 0.70, especially the Multilayer Perceptron Artificial Neural 
                         Network algorithm (Kappa = 0.87). This workflow demands low time 
                         processing and has potential to be reproduced for other study 
                         sites or SAR images obtained at different wavelengths.",
  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/3TUP9NS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3TUP9NS",
           targetfile = "97268.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "28 abr. 2024"
}