Classification of simulated radar imagery using lateral inhibition neural networks

The use of neural networks for the classification of simulated inverse synthetic aperture radar imagery is investigated. Symmetries of the artificial imagery make the use of localized moments a convenient preprocessing tool for the inputs to a neural network. A database of simulated targets was obtained by warping dynamical models to representative angles and generating images with differing target motions. Ordinary backward propagation (BP) and some variants of BP which incorporate lateral inhibition (LIBP) obtain a generalization rate of up to approximately 77% for novel data not used during training, a rate which is comparable to the mean level of classification accuracy that trained human observers obtained from the unprocessed simulated imagery. The authors also describe preliminary results for an unsupervised lateral inhibition network based on the BCM neuron. The feature vectors found by BCM are qualitatively different from those of BP and LIBP.<>