Pixel Classification of Satellite Images Using a Novel Pair Wise Kernel Function Svm

In this paper we have proposed a symmetric, positive semi definite kernel function for support vector machine classifier. Pixel classification is a form of supervised image segmentation where the actual object classes present in the image are known a priori. In case of satellite image, this prior information plays a huge role to estimate the actual statistics of different land covers. The state of the art kernels have the problem to clearly separate closely spaced data points, as in the case of image pixels of satellite images, where there are no sharp changes between two different regions in terms of the pixel intensity. The proposed kernel has overcome this difficulty with the previous kernels effectively and has good generalization capability. Experimental results establishes the fact when the proposed kernel based SVM has been used for supervised satellite image segmentation purpose.