@InProceedings{GirolamoNetoPessKörtFons:2016:DeAtFo,
               author = "Girolamo Neto, Cesare Di and Pess{\^o}a, Ana Carolina Moreira and 
                         K{\"o}rting, Thales Sehn and Fonseca, Leila Maria Garcia",
          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)}",
                title = "Detecting atlantic forest patches applying geobia and data mining 
                         techniques",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "GEOBIA 2016. : Solutions and Synergies",
             keywords = "Land cover, Classification, Landsat-8, Random Forest, Artificial 
                         Neural Networks, Feature selection.",
             abstract = "Brazilian Atlantic Forest is one of the most devastated tropical 
                         forests in the world. Considering that approximately only 12% of 
                         its original extent still exists, studies in this area are highly 
                         relevant. In this context, this study maps the land cover of 
                         Atlantic Forest within the Protected Area of Maca{\'e} de Cima, 
                         in Rio de Janeiro State, Brazil, combining GEOBIA and data mining 
                         techniques on an OLI/Landsat-8 image. The methodology proposed in 
                         this work includes the following steps: (a) image pan-sharpening; 
                         (b) image segmentation; (c) feature selection; (d) classification 
                         and (e) model evaluation. A total of 15 features, including 
                         spectral information, vegetation indices and principal components 
                         were used to distinguish five patterns, including Water, Natural 
                         forest, Urban area, Bare soil/pasture and Rocky mountains. 
                         Features were selected considering well-known algorithms, such as 
                         Wrapper, the Correlation Feature Selection and GainRatio. 
                         Following, Artificial Neural Networks, Decision Trees and Random 
                         Forests classification algorithms were applied to the dataset. The 
                         best results were achieved by Artificial Neural Networks, when 
                         features were selected through the Wrapper algorithm. The global 
                         classification accuracy obtained was of 98.3%. All the algorithms 
                         presented great recall and precision values for the Natural 
                         forest, however the patterns of Urban area and Bare soil/pastures 
                         presented higher confusion.",
  conference-location = "Enschede",
      conference-year = "14-16 set.",
                  doi = "10.13140/RG.2.2.21067.39206",
                  url = "http://dx.doi.org/10.13140/RG.2.2.21067.39206",
                label = "lattes: 5156610731557884 1 GirolamoNetoPessKortFons:2016:DeAtFo",
             language = "en",
           targetfile = "girolamo_detecting.pdf",
                  url = "http://proceedings.utwente.nl/362/",
        urlaccessdate = "27 abr. 2024"
}