Regularized Methods for Hyperspectral Image Classification

In this paper, we analyze regularized non-linear methods in the context of hyperspectral image classification. For this purpose, we compare regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVM), and kernel Fisher discriminant (KFD) analysis both theoretically and experimentally. We focus on the accuracy of methods when working in noisy environments, high input dimension, and limited number of training samples. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden, and the capability of the methods to provide probabilistic outputs. Although in general all methods yielded satisfactory results, SVM revealed more effective than KFD and RegRBFNN in standard situations regarding accuracy, robustness, sparsity, and computational cost.