Using Wavelet Support Vector Machine for Classification of Hyperspectral Images

Support vector machine (SVM) is a machine learning algorithm, which has been used recently for classification of hyperspectral images. SVM uses various kernel functions like RBF and polynomial to map the data into higher dimensional space to improve data separability. New kernel functions are used in this paper to classify hyperspectral images which are based on wavelet functions as named wavelet-kernels. The experimental results indicate that wavelet-kernels provide better classification accuracy than previous kernels.