Optimization of nonlinear Kernel PCA feature extraction algorithms for automatic target recognition

We present a multi-stage automatic target recognition (ATR) system using a kernel-based PCA (kPCA) for nonlinear feature extraction. The kPCA method uses a nonlinear kernel function to map data onto a higher dimensional space and then performs the PCA in the feature space. An algorithm for inserting kernel PCA into the existing ATR system was designed and various types of kernels were tested and optimized on several testing image sets such as video images of boats in choppy waves or approaching helicopters. We discuss the performance comparisons and trade-offs in using kPCA for ATR operations. kPCA generally outperforms normal PCA in classification accuracy and free-response receiver operating characteristics (FROC).