Eigentargets Versus Kernel Eigentargets: Detection of Infrared Point Targets Using Linear and Nonlinear Subspace Algorithms

摘要

The Eigentargets method, based on the linear principal component analysis (LPCA), has been used successfully to detect infrared point targets. LPCA is based only on the second-order correlations without taking higher-order statistics into account. That results in the limitation of Eigentargets in target detection. This paper extends Eigentargets, a linear subspace method, to kernel Eigentargets, a detection method based on a nonlinear subspace algorithm. Because the kernel Eigentargets is capable of capturing the part of higher-order statistics, the better detection performance can be achieved. Moreover, the Gaussian intensity model is modified to generate training samples of infrared point targets.