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@Article{Brum-BastosRibPinKörFon:2016:ImEvCe,
               author = "Brum-Bastos, V. S. and Ribeiro, B. M. G. and Pinheiro, C. M. D. 
                         and K{\"o}rting, Thales Sehn and Fonseca, Leila Maria Garcia",
          affiliation = "{University of St. Andrews} and {Universidade Federal do Rio 
                         Grande do Sul (UFRGS)} and {Universidade Federal do ABC (UFABC)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improvement evaluation on ceramic roof extraction using 
                         WorldView-2 imagery and geographic data mining approach",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2016",
               volume = "41",
                pages = "883--889",
                month = "July",
                 note = "3rd International Archives of the Photogrammetry, Remote Sensing 
                         and Spatial Information Sciences Congress, ISPRS 2016; Prague; 
                         Czech Republic; 12 -19 July 2016.",
             keywords = "C4.5, Ceramic roof, Classification accuracy, Decision tree, 
                         GEOBIA, Geographical data mining, WorldView-2.",
             abstract = "Advances in geotechnologies and in remote sensing have improved 
                         analysis of urban environments. The new sensors are increasingly 
                         suited to urban studies, due to the enhancement in spatial, 
                         spectral and radiometric resolutions. Urban environments present 
                         high heterogeneity, which cannot be tackled using pixel-based 
                         approaches on high resolution images. Geographic Object-Based 
                         Image Analysis (GEOBIA) has been consolidated as a methodology for 
                         urban land use and cover monitoring; however, classification of 
                         high resolution images is still troublesome. This study aims to 
                         assess the improvement on ceramic roof classification using 
                         WorldView-2 images due to the increase of 4 new bands besides the 
                         standard {"}Blue-Green-Red-Near Infrared{"} bands. Our methodology 
                         combines GEOBIA, C4.5 classification tree algorithm, Monte Carlo 
                         simulation and statistical tests for classification accuracy. Two 
                         samples groups were considered: 1) eight multispectral and 
                         panchromatic bands, and 2) four multispectral and panchromatic 
                         bands, representing previous high-resolution sensors. The C4.5 
                         algorithm generates a decision tree that can be used for 
                         classification; smaller decision trees are closer to the semantic 
                         networks produced by experts on GEOBIA, while bigger trees, are 
                         not straightforward to implement manually, but are more accurate. 
                         The choice for a big or small tree relies on the user's skills to 
                         implement it. This study aims to determine for what kind of user 
                         the addition of the 4 new bands might be beneficial: 1) the common 
                         user (smaller trees) or 2) a more skilled user with coding and/or 
                         data mining abilities (bigger trees). In overall the 
                         classification was improved by the addition of the four new bands 
                         for both types of users.",
                  doi = "10.5194/isprsarchives-XLI-B7-883-2016",
                  url = "http://dx.doi.org/10.5194/isprsarchives-XLI-B7-883-2016",
                 issn = "1682-1750",
             language = "en",
        urlaccessdate = "23 nov. 2020"
}


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