Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition - Koch, Mark W.; Moya, Mary M.; Hostetler, Larry D.; Fogler, R.Joseph (Neural networks, 1995)
The exquisite capabilities of biological neural systems for recognizing target patterns subject to large variations have motivated us to investigate neurophysiologically-inspired techniques for automatic target recognition. This paper describes a modular multi-stage architecture for focus-of-attention cueing, feature discovery and extraction, and one-class pattern learning and identification in synthetic aperture radar imagery. To prescreen massive amounts of image data, we apply a focus-of-attention algorithm using data skewness to extract man-made objects from natural clutter regions. We apply self-organizing feature discovery algorithms that uniquely characterize targets in a reduced dimension space and use self-organizing one-class classifiers for learning target variations. We also develop a distance metric for partial obscuration recognition. We present performance results using simulated SAR data and test for within-class generalization using nontrained targets including both in-the-clear and partially obscured examples. We test for between-class generalization using non-trained targets including both in-the-clear and partially obscured examples. We test for between-class generalization using near-target data.