Evaluating Kernel Effect on Performance of SVM Classification using Satellite Images
Support Vector Machine (SVM) a statistical learning algorithm has proved its excellencies in almost every domain towards of Image classification on various data formats. However, satellite images are more complex due to its number of bands that really contribute to image classification problem. Supervised approaches for satellite images needs more precise, fast and efficient machine learning models. Even though, SVM is non parametric binary classifier, but could be mould to resolve multi class classification problems. SVM comprise of superior machine learning algorithms group based upon the mean field theory, which basically uses quadratic optimization technique. SVM kernels are used to demarcate optimum hyperplane in non linear problem space, which in return generalize to unseen samples with least error among all possible boundaries separating two classes. Kernel efficiency varies with SVM implementations like CSVC, nu SVC, nu SVR, single class SVM, and CS SVM. Use of density estimation function along with various norms, have great impact on SVM kernel performance for classification problems. Kernel tricks very well handle the problem of mixed pixels, which is vital affecting source on classification accuracies. Research illustrate that SVM kernel type sigmoid perform better than other SVM kernel types like Polynomial, Radial Basic, and Linear kernel at fixed training data size of 300 pixel/class. It is most appropriate in situations where the training sample data are difficult to collect, as it works better even with small number of training samples. Perhaps, 75 to 100 pixels per class are recommended for accuracy assessment of classification.