@InProceedings{NascimentoCruz:2013:MiDaAd,
               author = "Nascimento, L{\'{\i}}dice Cabral and Cruz, Carla Bernadete 
                         Madureira",
                title = "Minera{\c{c}}{\~a}o de dados e adapta{\c{c}}{\~a}o de modelos 
                         de classifica{\c{c}}{\~a}o de cobertura e uso da terra para 
                         imagem Worldview 2",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "2345--2352",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The studies about forest fragmentation have increased since the 
                         past 3 decades, together with the increase of the discussion about 
                         conservation and preservation. In this way, the landscape and land 
                         use maps are important tools for this analysis, as well as other 
                         remote sensing techniques. The object oriented analysis classifies 
                         the image according to patterns as texture, color, shape, and 
                         context. To identify which value and attribute is necessary to the 
                         classification, data mining technique has been used, because it 
                         looks for patterns inside the data. This technique helps to 
                         accelerate the process; however, it was necessary to adapt the 
                         model. In this way, the land use and land cover classification was 
                         made using the values that data mining software has provided. 
                         Afterwards, this classification was analyzed, and the verification 
                         was made by the confusion matrix, which was generated through the 
                         creation of random points shapefile in ArcGis 9.3, trough this it 
                         was possible to evaluate the classification accuracy (58,89%). 
                         From these results, the model has been adapted. However, these 
                         adaptations have been done in the classes water and forest. As 
                         result, 2 new classes have been created: shadow and forest2. Also, 
                         some values were changed and some attributes added, for example, 
                         brightness, to identify dark areas. In the end, the accuracy was 
                         89,33%, however this result doesnt show some errors which are 
                         still on the model.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "789",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GFTQ",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GFTQ",
           targetfile = "p0789.pdf",
                 type = "Classifica{\c{c}}{\~a}o e Minera{\c{c}}{\~a}o de Dados",
        urlaccessdate = "04 maio 2024"
}