@InProceedings{KuxPinh:2006:CaStSã,
               author = "Kux, Hermann Johann Heirinch and Pinho, Carolina Moutinho Duque de 
                         Pinho",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Object-oriented analysis of high-resolution satellite images for 
                         intra-urban land cover classification: case study in S{\~a}o 
                         Jos{\'e} dos Campos, S{\~a}o Paulo State, Brazil",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "S. Lang, T. Blaschke \& E. Sch{\"o}pfer",
         organization = "International Conference on Object-based Image Analysis, 1. 
                         (OBIA2006)",
            publisher = "Z_GIS, Salzburg University (Austria)",
              address = "Salzburg",
             keywords = "Sensoriamento Remoto, SIG, Imagens de alta resolu{\c{c}}{\~a}o, 
                         Classifica{\c{c}}{\~a}o orientada a objeto, L{\'o}gica fuzzy.",
             abstract = "The detailed analysis of urban regions is among those areas which 
                         most benefited from the availability of high-resolution satellite 
                         data such as e.g. IKONOS and QUICKBIRD. These data offer as well 
                         high spatial, radiometric and temporal resolution, competing with 
                         aerial photographs for several applications. Merging these 
                         characteristics allows the detection of intra-urban targets and 
                         consequently proves to be suitable for mapping urban and 
                         intra-urban land cover using automatic classifiers. Taking into 
                         account the huge volume of data at each scene from these sensor 
                         systems (11 bits, 2048 gray levels) the conventional 
                         pixel-by-pixel classifiers, considering only spectral 
                         characteristics, show clear limitations for classification tasks. 
                         An alternative to this shortcoming is the incorporation of other 
                         types of attributes to the classification process, such as shape, 
                         size, color and contextual information. Being so, we used an 
                         object-oriented classifier from software package eCognition 4.0 
                         which is an effective option, since it uses both topologic 
                         (neighborhood, context) and geometric information (shape and 
                         size). In this frame, an image classification experiment was 
                         conducted for test-site S{\~a}o Jos{\'e} dos Campos, S{\~a}o 
                         Paulo State (Brazil), where a classification scheme was conceived 
                         and applied using both IKONOS and QUICKBIRD data. The 
                         classification results were compared and evaluated in order to 
                         assess which sensor allows best classification performance in such 
                         a highly complex and heterogeneous environment.",
  conference-location = "Salzburg, Austria",
      conference-year = "July 4-5 2006",
             language = "en",
         organisation = "Z_GIS",
           targetfile = "kux_object.pdf",
               volume = "(CD)",
        urlaccessdate = "15 jun. 2024"
}