SVDD-based land-cover mapping using optimal parameters via single window flexible pace search method

The SVDD method, one of the most popular one-class classifiers, could use training samples of the interest class to derive accurate classification and thus is adopted in this paper. However, the penalty parameter C and the kernel width s should be tuned carefully to construct an optimal hypersphere. This research developed a single window flexible pace search method to select optimal parameters. First, 120 edge pixels were acquired from parcel boundary and PCA image. Then a 3*3 window was applied to the training samples to obtain the buffer training set. Then optimal parameters were select through the flexible pace search method. Under optimal parameters, the buffer training set yielded an accurate classification with an overall accuracy of 89.70%, which differed slightly with that derived from the SVM classification. Thus, we conclude that our proposed method could be used to select optimal parameters for the SVDD method.