@InProceedings{EspindolaCâmReiBinMon:2005:SpAuIn,
               author = "Espindola, Giovana and C{\^a}mara, Gilberto and Reis, Ilka and 
                         Bins, Leonardo and Monteiro, Ant{\^o}nio Miguel Vieira",
          affiliation = "Instituto Nacional de Pesquisas Espaciais, Divis{\~a}o de 
                         Processamento de Imagens (INPE, DPI)",
                title = "Spatial Autocorrelation Indicators for Evaluation of Remote 
                         Sensing Image Segmentation Algorithms",
            booktitle = "Proceedings...",
                 year = "2005",
                pages = "117--121",
         organization = "GIS and Spatial Analysis - 2005. Annual Conference of the 
                         International Association for Mathematical Geology, IAMG 2005",
            publisher = "IAMG",
             keywords = "Algorithms, Autocorrelation, Geology, Image reconstruction, 
                         Quality control, Evaluation of segmentation, Extracting 
                         information, Remote sensing images, Segmentation algorithms, 
                         Segmentation quality, Segmentation results, Similarity criteria, 
                         Spatial autocorrelations, Image segmentation.",
             abstract = ": Segmentation algorithms have been often used for extracting 
                         information in remote sensing images. Segmentation consists in a 
                         process where the pixels of an image are grouped into homogeneous 
                         contiguous areas, based on similarity criteria. The resulting 
                         image can then be transformed into vector maps by defining spatial 
                         objects associated to the contiguous areas. The performance of 
                         segmentation algorithms is strongly dependent on ad-hoc parameters 
                         provided by the user. As a consequence, evaluation of segmentation 
                         results is a non trivial task, and for that reason it is very 
                         important to devise techniques to evaluate the quality of 
                         segmentation algorithms and their parameters. A method for 
                         evaluating segmentation quality is presented and used to compare 
                         image segmentation results. This method considers that a good 
                         segmentation has two qualities from a spatial statistics 
                         viewpoint: The resulting regions should have internal homogeneity 
                         and should be distinguishable from its neighborhood. In such 
                         perspective, we propose the use of spatial autocorrelation 
                         indicators as a tool for evaluating the quality of segmentation 
                         algorithms.",
  conference-location = "Toronto",
      conference-year = "21-26 Aug",
                 isbn = "0973422025 and 9780973422023",
                label = "self-archiving-INPE-MCTIC-GOV-BR",
             language = "en",
         organisation = "China Univ. of Geosciences, State Key Lab of; Geological Processes 
                         and Mineral Resources; Geological Survey of Canada; International 
                         Association for Mathematical Geology; University of Toronto, 
                         Department of Geology; York University",
        urlaccessdate = "12 maio 2024"
}