Lateral inhibition neural networks for classification of simulated radar imagery

Abstract

The use of neural networks for the classification of simulated inverse synthetic aperture radar (ISAR) imagery is investigated. Certain 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 is obtained by warping dynamical models to representative angles and generating images with different target motions. Ordinary backward propagation (BP) and some variants of BP which incorporate lateral inhibition obtain a generalization rate of up to approximately 78% for novel data not used during training, a rate which is comparable to the level of classification accuracy that trained human observers obtained from the unprocessed simulated imagery.<>