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@Article{KörtingFonsCastNami:2014:ImSaSe,
               author = "K{\"o}rting, Thales Sehn and Fonseca, Leila Maria Garcia and 
                         Castejon, Emiliano Ferreira and Namikawa, Laercio Massaru",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Improvements in Sample Selection Methods for Image 
                         Classification",
              journal = "Remote Sensing",
                 year = "2014",
               volume = "6",
               number = "8",
                pages = "7580--7591",
             keywords = "image classification, sample selection, remote sensing, Graphical 
                         User Interface (GUI).",
             abstract = "Traditional image classification algorithms are mainly divided 
                         into unsupervised and supervised paradigms. In the first paradigm, 
                         algorithms are designed to automatically estimate the classes 
                         distributions in the feature space. The second paradigm depends on 
                         the knowledge of a domain expert to identify representative 
                         examples from the image to be used for estimating the 
                         classification model. Recent improvements in human-computer 
                         interaction (HCI) enable the construction of more intuitive 
                         graphic user interfaces (GUIs) to help users obtain desired 
                         results. In remote sensing image classification, GUIs still need 
                         advancements. In this work, we describe our efforts to develop an 
                         improved GUI for selecting the representative samples needed to 
                         estimate the classification model. The idea is to identify changes 
                         in the common strategies for sample selection to create a 
                         user-driven sample selection, which focuses on different views of 
                         each sample, and to help domain experts identify explicit 
                         classification rules, which is a well-established technique in 
                         geographic object-based image analysis (GEOBIA). We also propose 
                         the use of the well-known nearest neighbor algorithm to identify 
                         similar samples and accelerate the classification.",
                  doi = "10.3390/rs6087580",
                  url = "http://dx.doi.org/10.3390/rs6087580",
                 issn = "2072-4292",
                label = "lattes: 8609036872819243 1 K{\"o}rtingFonsCastNami:2014:ImSaSe",
             language = "en",
           targetfile = "remotesensing-06-07580thales.pdf",
        urlaccessdate = "26 nov. 2020"
}


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