Analysing the Sensitivity of SVM Kernels on Hyperspectral Imagery for Land Use Land Cover Classification

Abstract

Support Vector Machine is a non- parametric statistical learning theory-based machine learning technique that classifies the data by maximising the margin between two classes by constructing a hyperplane between them. Although SVM’s are well known for binary classification problems, for multiclass classification problems- they use certain kernels, to turn the linear boundaries into non-linear boundaries in a higher dimensional feature space. In this context, the type of kernel used in non-linear SVMs have a profound effect on final performance of SVM classifier. The current study focuses on analysing the sensitivity of various linear and non-linear SVM kernels and parameters used in them for classifying 10 different land use/cover features from remotely sensed space borne hyperspectral image. Forty five different models were tested to analyse the performance of each kernel with varying penalty values for classification of land use/land cover classes. Analysis of the classified maps was made based on the final overall accuracy, error/penalty parameter and degree of polynomial. Experimental results show that SVM with RBF kernel outperformed the other kernels with an overall accuracy of 90.63%, with an error penalty of 100 and a gamma value of 0.006 followed by polynomial kernel of degree 2.