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@Article{NegriDutrSantLu:2016:ExReMe,
               author = "Negri, Rog{\'e}rio G. and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira and Lu, D.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Michigan State University}",
                title = "Examining region-based methods for land cover classification using 
                         stochastic distances",
              journal = "International Journal of Remote Sensing",
                 year = "2016",
               volume = "37",
               number = "8",
                pages = "1902--1921",
                month = "Apr.",
             keywords = "Graph theory, Pixels, Radar imaging, Remote sensing, Stochastic 
                         systems, Support vector machines, Synthetic aperture radar.",
             abstract = "A recent alternative to standard pixel-based classification of 
                         remote-sensing data is region-based classification, which has 
                         proved to be particularly useful when analysing high-resolution 
                         imagery of complex environments, such as urban areas, or when 
                         addressing noisy data, such as synthetic aperture radar (SAR) 
                         images. First, following certain criteria, the imagery is 
                         decomposed into homogeneous regions, and then each region is 
                         classified into a class of interest. The usual method for 
                         region-based classification involves using stochastic distances, 
                         which measure the distances between the pixel distributions inside 
                         an unknown region and the representative distributions of each 
                         class. The class, which is at the minimum distance from the 
                         unknown region distribution, is assigned to the region and this 
                         procedure is termed stochastic minimum distance classification 
                         (SMDC). This study reports the use of methods derived from the 
                         original SMDC, Support Vector Machine (SVM), and graph theory, 
                         with the objective of identifying the most robust and accurate 
                         classification methods. The equivalent pixel-based versions of 
                         region-based analysed methods were included for comparison. A case 
                         study near the Tapaj{\'o}s National Forest, in Par{\'a} state, 
                         Brazil, was investigated using ALOS PALSAR data. This study showed 
                         that methods based on the nearest neighbour, derived from SMDC, 
                         and SVM, with a specific kernel function, are more accurate and 
                         robust than the other analysed methods for region-based 
                         classification. Furthermore, pixel-based methods are not indicated 
                         to perform the classification of images with a strong presence of 
                         noise, such as SAR images.",
                  doi = "10.1080/01431161.2016.1165883",
                  url = "http://dx.doi.org/10.1080/01431161.2016.1165883",
                 issn = "0143-1161",
             language = "en",
        urlaccessdate = "05 dez. 2020"
}


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