Abstract
The problem of the automatic classification of superresolution
ISAR images is addressed in the paper. We describe an anechoic
chamber experiment involving ten-scale-reduced aircraft models.
The radar images of these targets are reconstructed using MUSIC-2D
(multiple signal classification) method coupled with two
additional processing steps: phase unwrapping and symmetry
enhancement. A feature vector is then proposed including Fourier
descriptors and moment invariants, which are calculated from the
target shape and the scattering center distribution extracted from
each reconstructed image. The classification is finally performed
by a new self-organizing neural network called SART (supervised
ART), which is compared to two standard classifiers, MLP
(multilayer perceptron) and fuzzy KNN (K nearest neighbors).
While the classification accuracy is similar, SART is shown to
outperform the two other classifiers in terms of training speed
and classification speed, especially for large databases. It is
also easier to use since it does not require any input parameter
related to its structure.