Kernel-Based KNN and Gaussian Classifiers for Hyperspectral Image Classification

In this study, two kernel-based classifiers are applied to hyperspectral image classification. One is the kernel Gaussian classifier, and the other is the kernel k-nearest-neighbor classifier. For classification in feature space, the data are mapped from the input-space into a higher dimensional feature space by utilizing a nonlinear transformation, and then we can perform the k-nearest-neighbor classifier and the Gaussian classifier on the mapped images in that space. Fortunately, instead of doing the expensive transformation of samples, the classification can be performed via inner products in feature space and use the kernel function to efficiently compute the inner products which is the so-called kernel trick. The effectiveness of the proposed classifiers is evaluated by real datasets and other classifiers are included for comparison. The experimental results show that the kernel Gaussian classifier outperforms the others.