Joint compression and discrimination algorithm for clutter rejection
Many pattern recognition (ATR) systems perform dimensionality reduction on the input imagery to reduce the requirement for a large number of training samples and high computational cost. Most of the dimensionality reduction techniques seek to optimize only the overall data compression, but not the interclass discriminability. We present a neural-network-based algorithm that simultaneously achieves data compression and target discriminability by adjusting the pre-trained base components to maximize separability between classes. This allows the classifiers to operate at a higher level of efficiency and generalization capability on low-dimensional data. We have applied this technique to the problem of automatic detection of military vehicles in infrared imagery.