@InProceedings{LeonardiAlmFonTomOli:2012:GeAlDa,
               author = "Leonardi, Fernando and Almeida, Claudia Maria and Fonseca, Leila 
                         Maria Garcia and Tomas, Livia and Oliveira, Cleber",
          affiliation = "{} and undefined and undefined",
                title = "Genetic algorithms and data mining applied to optical orbital and 
                         LiDAR data for object-based classification of urban land cover",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "649--654",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Laser Scanning, Decision Tree, Semantic Network, Semi-Automated 
                         Classification.",
             abstract = "The study of the urban environment has raised great interest among 
                         researchers and practitioners involved with the use of remote 
                         sensing, in face of the challenges for its investigation and the 
                         complexity of its targets. Although they have great potential for 
                         studies of urban environments, the high-resolution images present 
                         difficulties for automatic extraction of information because they 
                         are characterized by high spatial and spectral heterogeneity for 
                         the same segment, which greatly complicates segmentation and 
                         classification processes. Thus, new concepts and analyses have 
                         been used for mapping the urban space. Object-based image analysis 
                         and multiresolution segmentation have been quite efficient in the 
                         discrimination of urban targets in high spatial resolution images. 
                         One technique that can assist the classification process is data 
                         mining, which can be used to explore large data sets, identify and 
                         characterize patterns of interest, and hence, support the precise 
                         extraction of useful information. In this context, this paper 
                         proposes a methodology jointly employing cognitive approaches 
                         (semantic net, object-based image analysis) and data mining 
                         (genetic algorithms and decision trees) for the classification of 
                         urban land cover from optical orbital and airborne laser data. To 
                         assess the efficacy of the methodology and ensure the accuracy of 
                         the produced maps, the steps undertaken in this study were subject 
                         to quality control. The results were presented and discussed, 
                         indicating a satisfactory accuracy in the generated mapping 
                         products, demonstrating the reliability of the methodology for 
                         mapping land cover in urban areas.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BTBAG8",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BTBAG8",
           targetfile = "179.pdf",
                 type = "LiDAR and SAR Applications",
        urlaccessdate = "20 maio 2024"
}