@InProceedings{AnjosAlmeGalv:2015:IdMaUr,
               author = "Anjos, Camila Souza and Almeida, Cl{\'a}udia Maria de and 
                         Galv{\~a}o, L{\^e}nio Soares",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Identifica{\c{c}}{\~a}o de materiais urbanos por meio de 
                         m{\'e}todos inovadores de classifica{\c{c}}{\~a}o de imagens",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4377--4384",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Urban areas represent one of the most challenging environments for 
                         remote sensing analysis. In general, urban areas represent 
                         spatially and spectrally complex environments, where exposed 
                         targets show profuse geometric and compositional variations. In 
                         this context, the use of images with high spatial and spectral 
                         resolutions is an excellent alternative for urban studies. The 
                         combination of these imagery characteristics allow a potential 
                         improvement for detection and discrimination of urban targets, 
                         especially using automatic classifiers. This research uses optical 
                         ultispectral data with very high spatial resolution (VHR) acquired 
                         by the WorldView-2 satellite in eight spectral channels. The study 
                         area is a transect in the campus of the State University of 
                         Campinas (UNICAMP), located in the Campinas municipality, 
                         southeast of S{\~a}o Paulo State Brazil. The area comprises a 
                         diversity of urban targets, such as French tiles, metal roofs, 
                         concrete/asphalt, water, low vegetation, woody vegetation, among 
                         others. The imagery dataset were processed by means of 
                         nonparametric classifiers. Three classification experiments were 
                         performed using the following techniques: (i) Decision Tree, 
                         Support Vector Machines (SVM), and (ii) Random Forest (RF). A 
                         comparative evaluation among these three nonparametric 
                         classification methods was also produced, seeking to examine the 
                         confusion matrix and the Kappa index. The results indicated that 
                         all classifiers showed high performance with Kappa values greater 
                         than 0.8. The SVM got the best Kappa result (0.93).",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "859",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CN2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4CN2",
           targetfile = "p0859.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}