@Article{NegriDutrSant:2014:InSuVe,
               author = "Negri, Rogerio Galante and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Joao Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "An innovative support vector machine based method for contextual 
                         image classification",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2014",
               volume = "87",
                pages = "241--248",
                month = "Jan.",
             keywords = "image classification, contextual information, support vector 
                         machine.",
             abstract = "Several remote sensing studies have adopted the Support Vector 
                         Machine (SVM) method for image classification. Although the 
                         original formulation of the SVM method does not incorporate 
                         contextual information, there are different proposals to 
                         incorporate this type of information into it. Usually, these 
                         proposals modify the SVM training phase or make an integration of 
                         SVM classifications using stochastic models. This study presents a 
                         new perspective on the development of contextual SVMs. The main 
                         concept of this proposed method is to use the contextual 
                         information to displace the separation hyperplane, initially 
                         defined by the traditional SVM. This displaced hyperplane could 
                         cause a change of the class initially assigned to the pixel. To 
                         evaluate the classification effectiveness of the proposed method a 
                         case study is presented comparing the results with the standard 
                         SVM and the SVM post-processed by the mode (majority) filter. An 
                         ALOS/PALSAR image, PLR mode, acquired over an Amazon area was used 
                         in the experiment. Considering the inner area of test sites, the 
                         accuracy results obtained by the proposed method is better than 
                         SVM and similar to SVM post-processed by the mode filter. The 
                         proposed method, however, produces better results than mode 
                         post-processed SVM when considering the classification near the 
                         edges between regions. One drawback of the method is the 
                         computational cost of the proposed method is significantly greater 
                         than the compared methods.",
                  doi = "10.1016/j.isprsjprs.2013.11.004",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2013.11.004",
                 issn = "0924-2716",
                label = "isi 2014-05 NegriDutrSiqu:2014:InSuVe",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}