@InProceedings{CohencaCarv:2015:CoMéCl,
               author = "Cohenca, Daniel and Carvalho, Raquel",
                title = "Compara{\c{c}}{\~a}o de m{\'e}todos de 
                         classifica{\c{c}}{\~a}o OBIA, M{\'a}xima Verossimilhan{\c{c}}a 
                         e Dist{\^a}ncia M{\'{\i}}nima em imagem OLI/Landsat-8 em 
                         {\'a}rea de alta diversidade de uso do solo",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1035--1042",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The great demand for reliable thematic maps requires testing 
                         different methods of classification to determine the most 
                         appropriate considering land use diversity, research objectives, 
                         satellite images available and time invested. To identify the most 
                         appropriate classification method for an OLI/Landsat-8 satellite 
                         image covering a coastal region in Santa Catarina state, Brazil, 
                         three methods were tested for their accuracy: Maximum Likelihood 
                         and Minimum Distance, pixel based supervised classifications, and 
                         Object Based Image Analysis (OBIA) based on image segmentation 
                         into regions and separation of classes based on geometric, 
                         textural and spectral values. The accuracy assessment for each 
                         classification method was based in a stratified random independent 
                         sampling to build an error matrix for the global and conditional 
                         (per class) Kappa coefficients calculation. In agreement with our 
                         results, the maximum likelihood and OBIA classifiers had a better 
                         performance in comparison to Minimum Distance method. By other 
                         hand, the poorest performance of the Minimum Distance method 
                         suggests that the use of spectral mean value only to classify 
                         single pixels is not appropriate to the classification process. 
                         Although differences of Global Kappa coefficients between OBIA and 
                         MaxVer methods were not significant, the assessment of conditional 
                         Kappa coefficients for each class evidenced significant 
                         differences. Structural differences between these two methods are 
                         discussed regarding the achieved results and also on the 
                         considerations around time-consuming efforts of an interpreter in 
                         the case of OBIA.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "193",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM47M5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM47M5",
           targetfile = "p0193.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "15 jun. 2024"
}