@InProceedings{RibeiroCentMend:2017:OtClSu,
               author = "Ribeiro, B{\'a}rbara Maria Giaccom and Centeno, Jorge Antonio 
                         Silva and Mendes, Carlos Andr{\'e} Bullh{\~o}es",
                title = "Otimiza{\c{c}}{\~a}o de classifica{\c{c}}{\~a}o supervisionada 
                         da cobertura do solo em S{\~a}o Leopoldo (RS) por meio de 
                         sele{\c{c}}{\~a}o de conjuntos de dados m{\'{\i}}nimos",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1321--1328",
         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 = "Recent developments in geotechnologies have provide resources to 
                         propose innovative strategies for urban and environmental 
                         management, including remote sensing data and computational 
                         resources for processing them, which together, can generate 
                         high-quality map products and valuable databases. For the purpose 
                         of mapping the Earth''s surface, digital image processing and 
                         classification enables information extraction through recognition 
                         of patterns and objects related to features of interest. The 
                         practical use of large volumes of orbital data implies, however, 
                         some costs, for example, the computational cost, which is 
                         generally high, and is required for data processing and 
                         classification. In many cases, one faces a classification problem 
                         resulting from non-increase of results accuracy as the number of 
                         bands used (and therefore the amount of information available) 
                         increases. One possible solution lies in selecting a subset of 
                         features with more discriminating power among the available bands. 
                         The aim of this study is to evaluate and compare the performance 
                         of land cover classification using a parametric classifier 
                         (Maximum Likelihood) using different sets of input data (i.e., 
                         number of spectral bands), extracted from two Landsat 8 images 
                         (dry × rainy seasons), city of S{\~a}o Leopoldo, Rio Grande do 
                         Sul, Brazil. The data sets are defined based on the calculation of 
                         the transformed divergence. Finally, the results are analyzed 
                         statistically to assess the quality of the classifications.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59233",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS4GGC",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS4GGC",
           targetfile = "59233.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}