Comparing support vector machine contextual approaches for urban area classification
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2016-05-03
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Negri, R. G. [UNESP]
Dutra, L. V.
SantAnna, S. J.S.
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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.
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Remote Sensing Letters, v. 7, n. 5, p. 485-494, 2016.