Non-parametric error estimation techniques applied to MSTAR data sets

The development of ATR performance characterization tools is very important for the design, evaluation and optimization of ATR systems. One possible approach for characterizing ATR performance is to develop measures of the degree of separability of the different target classes based on the available multi-dimensional image measurements. One such measure is the Bayes error which is the minimum probability of misclassification. Bayes error estimates have previously been obtained using Parzen window techniques on real aperture, high range resolution (HRR), radar data sets and on simulated synthetic aperture radar (SAR) images. This report extends these results to real MSTAR SAR data. Our results show that the Parzen window technique is a good method for estimating the Bayes error for such large dimensional data sets. However, in order to apply non-parametric error estimation techniques, feature reduction is needed. A discussion of the relationship between feature reduction and non-parametric estimation is included in this paper. The results of multimodal Parzen estimation on MSTAR images are also described. The tools used to produce the Bayes error estimates have been modified to produce Neyman-Pearson criterion estimates as well. Receiver Operating Characteristic (ROC) curves are presented to illustrate non-parametric Neyman-Pearson error estimation on MSTAR images.