Ship target recognition using kernel Fisher discriminant

The classification of ship targets using the kernel Fisher discriminant analysis is investigated in this paper. The main idea of this method is to find a nonlinear direction by first mapping the data nonlinearly into some feature space and compute Fisher's linear discriminant there, thus implicitly yield a nonlinear discriminant in input space. Based on the kernel Fisher discriminant, we recognize three types of ships. The satisfactory experimental results are obtained. In addition, we compare this method with other state of the art classification techniques. The experiments show that the kernel Fisher discriminant (plus a support vector machine to estimate the threshold) is superior to the other algorithms.