@InProceedings{SartorioZano:2017:MéMuAd,
               author = "Sartorio, Let{\'{\i}}cia Figueiredo and Zanotta, Daniel Capella 
                         Capella",
                title = "M{\'e}todo multi-resolu{\c{c}}{\~a}o adaptativo para 
                         classifica{\c{c}}{\~a}o simult{\^a}nea de {\'a}reas rurais e 
                         urbanas",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7240--7247",
         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 = "The present work seeks to develop an adaptive classification 
                         method operating simultaneously with different rules in images 
                         including rural and urban areas. Traditional techniques are 
                         commonly based on the application of only one type of 
                         classification strategy. This assumption generally causes bad 
                         fitting of some areas if the image includes different kinds of 
                         targets. The aim is to obtain a more efficient thematic 
                         classification through the combined using of techniques and data 
                         of different resolutions, when compared with results achieved 
                         using a single approach. The proposed formulation is based on the 
                         premise that classification of rural and urban targets usually 
                         show large variations depending on how they are classified. Thus, 
                         traditional classifiers applied in environments that include rural 
                         and urban areas eventually end up benefiting one area over 
                         another. The first step of the suggested technique performs a 
                         prior automatic separation of urban and rural targets from the 
                         studied area, which will then be classified with different methods 
                         and input data. One experiment was performed using data with 
                         compatible resolution and classification techniques, according to 
                         the literature. Visual comparisons with classifications made only 
                         by means of one type of classification strategy leads us to 
                         visually verify the soundness of the suggested framework.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59367",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMFC9",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMFC9",
           targetfile = "59367.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}