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@Article{NovackKux:2010:UrLaCo,
               author = "Novack, T and Kux, H. J. H",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Urban land cover and land use classification of an informal 
                         settlement area using the open-source knowledge-based system 
                         InterIMAGE",
              journal = "Journal of Spatial Science",
                 year = "2010",
               volume = "55",
               number = "1",
                pages = "23 -41",
                month = "June",
             abstract = "This study uses the InterIMAGE system and imagery from the Quick 
                         Bird sensor for land cover and land use classification at two test 
                         sites with informal settlements in the metropolis of Sao Paulo, 
                         Brazil InterIMAGE is an open source and free access system for 
                         knowledge-based image classification Within InterIMAGE human 
                         knowledge is represented as a semantic net and by user-defined 
                         rules based on the paradigms of object-oriented image analysis In 
                         the land cover classification step, a genetic algorithm was used 
                         for determining appropriate segmentation parameters For the 
                         description of the land cover classes in terms of features and 
                         thresholds, a strategy combining machine learning algorithms and a 
                         semantic net was elaborated Based on the land cover 
                         classifications, the land use classifications were carried out 
                         considering the urban blocks of the test sites as the analysis 
                         units Customized features related to the composition and 
                         geometrical structures of the land cover objects within these 
                         blocks were used for the description of the land use classes The 
                         proposed methodology has been shown to be efficient for the 
                         automatic mapping of the land cover and land use in complex urban 
                         areas The land cover classifications achieved overall accuracies 
                         above 70 percent and Kappa indexes above 0.65. Referring to the 
                         land use classifications, overall accuracies above 87 percent and 
                         Kappa indexes above 0 71 were obtained This study has explored the 
                         main functionalities of the InterIMAGE system, presenting as 
                         potential for object-based and knowledge-based image 
                         classification.",
                  doi = "10.1080/14498596.2010.487640",
                  url = "http://dx.doi.org/10.1080/14498596.2010.487640",
                 issn = "1449-8596",
             language = "en",
           targetfile = "98-311-1-PB-1.pdf",
        urlaccessdate = "22 jan. 2021"
}


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