We describe the application of the multilayer perceptron (MLP) network
and a version of the adaptive resonance theory version 2-A (ART 2-A)
network to the problem of automatic aerial image recognition (AAIR). T
he classification of aerial images; independent of their positions and
orientations, is required for automatic tracking and target recogniti
on. Invariance is achieved by the use of different invariant feature s
paces in combination with supervised and unsupervised neural networks.
The performance of neural-network-based classifiers in conjunction wi
th several types of invariant AAIR global features, such as the Fourie
r-transform space, Zernike moments, central moments, and polar transfo
rms, are examined. The advantages of this approach are discussed. The
performance of the MLP network is compared with that of a classical co
rrelator. The MLP neural-network correlator outperformed the binary ph
ase-only filter (BPOF) correlator. It was found that the ART 2-A disti
nguished itself with its speed and its low number of required training
vectors. However, only the MLP classifier was able to deal with a com
bination of shift and rotation geometric distortions. (C) 1996 Optical
Society of America