MSTAR 10-Class classification and confuser and clutter rejection using SVRDM

We compare the performance of our SVRDM (support vector representation and discrimination machine, a new SVM classifier) to that of other popular classifiers on the moving and stationary target acquisition and recognition (MSTAR) synthetic aperture radar (SAR) database. We present new results for the 10-class MSTAR problem with confuser and clutter rejection. Much prior work on the 10-class database did not address confuser rejection. In our prior work [1], we presented our results on a benchmark three-class experiment with confusers to be rejected. In this paper, we extend results to the ten-class classification case with confuser and clutter rejection. Our SVRDM achieved perfect clutter rejection scores, but the clutter was not demanding. Energy-normalization, which was used in many prior algorithms, makes clutter chips similar to target chips and thus produces worse results. We do not energy-normalize data.