@Article{NegriDutrSant:2012:StApMi,
               author = "Negri, Rogerio Galanti and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Jo{\~A}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Stochastic Approaches of Minimum Distance Method for Region Based 
                         Classification",
              journal = "Lecture Notes in Computer Science",
                 year = "2012",
               volume = "7441",
               number = "2012",
                pages = "797--804",
                 note = "17th Iberoamerican Congress on Progress in Pattern Recognition, 
                         Image Analysis, Computer Vision, and Applications, CIARP 2012 and 
                         {Buenos Aires} and {3 September 2012through6 September 2012} and 
                         Code92323",
             keywords = "Classification process, Image simulations, Minimum average 
                         distance, Minimum distance, Region-based, Remote sensing image 
                         classification, Second variation, Simple approach, Simulation 
                         studies, Stochastic approach, stochastic distances, Imagens de 
                         Sensoriamento Remoto, Reconhecimento de Padroes, 
                         Segmenta{\c{c}}{\~a}o de imagens.",
             abstract = "Normally remote sensing image classification is performed 
                         pixelwise which produces a noisy classification. One way of 
                         improving such results is dividing the classification process in 
                         two steps. First, uniform regions by some criterion are detected 
                         and afterwards each unlabeled region is assigned to class of the 
                         {"}nearest{"} class using a so-called stochastic distance. The 
                         statistics are estimated by taking in account all the reference 
                         pixels. Three variations are investigated. The first variation is 
                         to assign to the unlabeled region a class that has the minimum 
                         average distance between this region and each one of reference 
                         samples of that class. The second is to assign the class of the 
                         closest reference sample. The third is to assign the most frequent 
                         class of the k closest reference regions. A simulation study is 
                         done to assess the performances. The simulations suggested that 
                         the most robust and simple approach is the second variation.",
                  doi = "10.1007/978-3-642-33275-3_98",
                  url = "http://dx.doi.org/10.1007/978-3-642-33275-3_98",
                 issn = "0302-9743",
                label = "lattes: 9840759640842299 2 NegriDutrSant:2012:StApMi",
             language = "en",
           targetfile = "Paper-PublishedVersion-74410797.pdf",
        urlaccessdate = "04 maio 2024"
}