A new approach to target recognition for LADAR data

We discuss target detection in LADAR intensity images. Thirteen features, eleven of which come from an asymmetric co-occurrence matrix, are extracted from region-of-interest windows in each image. Two methods of feature selection are applied to the extracted vectors. Random selection leads to a pair of selected features for a nearest-neighbor rule (1-nn) detector. Extended backpropagation leads to six selected features using a modified multilayered perceptron (MLP) network. The 1-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%.