A new approach to target recognition for LADAR data

We discuss target detection in LADAR intensity images. Thirteen features, e leven of which come from an asymmetric co-occurrence matrix, are extracted from region-of-interest windows in each image, Two methods of feature selec tion are applied to the extracted vectors. Random selection leads to a pair of selected features for a nearest-neighbor rule (I-nn) detector. Extended back-propagation leads to six selected features using a modified multilaye red perceptron (MLP) network, The I-nn detector achieves a test-error rate of about 16% at a false-alarm rate of 8%. The MLP has a test-error rate of about 12% with a false-alarm rate of 6%.