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A new approach to target recognition for LADAR data

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4 Author(s)
N. R. Pal ; Dept. of Comput. Sci., West Florida Univ., Pensacola, FL, USA ; T. C. Cahoon ; J. C. Bezdek ; L. Pal

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%

Published in:

IEEE Transactions on Fuzzy Systems  (Volume:9 ,  Issue: 1 )