@Article{NegriDutrSant:2016:CoSuVe,
               author = "Negri, Rog{\'e}rio G. and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Comparing support vector machine contextual approaches for urban 
                         area classification",
              journal = "Remote Sensing Letters",
                 year = "2016",
               volume = "7",
               number = "5",
                pages = "485--494",
                month = "May",
             abstract = "Support vector machine (SVM) has been receiving a great deal of 
                         attention for remote sensing data classification. Although the 
                         original formulation of this method does not incorporate 
                         contextual information, lately different formulations have been 
                         proposed to incorporate such information, with the aim of 
                         improving the mapping accuracy. In general, these proposals modify 
                         the SVM training phase or integrate the SVM classifications in 
                         stochastic models. Recently, two new contextual versions of SVM, 
                         context adaptive and competitive translative SVM (CaSVM and CtSVM, 
                         respectively), were proposed in literature. In this work, two case 
                         studies of urban area classification, using IKONOS-II and 
                         hyperspectral digital imagery collection experiment (HYDICE) data 
                         sets were conducted to compare SVM, SVM integrated with the 
                         iterated conditional modes (ICM) stochastic algorithm, SVM 
                         smoothed using the mode filter and the recent approaches CaSVM and 
                         CtSVM. The results indicated that although it possesses a high 
                         computational cost, the CaSVM method was able to produce 
                         classification results with similar accuracy (using kappa 
                         coefficient) to those obtained using SVM integrated with ICM 
                         (SVM+ICM) and the mode filter (SVM+Mode), all of them found 
                         statistically superior to the SVM result at 95% confidence level 
                         for the IKONOS-II image. For HYDICE image, all results were found 
                         statistically insignificant at 95% confidence level. Investigation 
                         of what happens at transition regions between classes, however, 
                         showed that some methods can present superior performance. To this 
                         objective, a new performance measure, called upsilon coefficient, 
                         was introduced in this work, which measures the impact that the 
                         smoothing effect, typical of contextual methods, can have in 
                         distorting the edges between regions. With this new measure was 
                         found that CaSVM is the one which has better performance followed 
                         with SVM+ICM.",
                  doi = "10.1080/2150704X.2016.1154218",
                  url = "http://dx.doi.org/10.1080/2150704X.2016.1154218",
                 issn = "2150-704X",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}