Determining training data requirements for template based normalized cross correlation

In this paper, we investigate the effect of increasingly sparse training data sets on target classification performance using a template-based classifier. An often used method of template creation employs averaging of multiple target training chips for a predefined coverage swath. The inclusion of too many training chips results in a blurring of the predominant scatterers while averaging of too few training chips results in poor edge resolution. We use the public MSTAR data set to show that using all appropriate images for each template may not result in the best ATR performance. We successfully demonstrate the ability to reduce training data collection requirements by requiring fewer training chips per template.