One-class classifiers and their application to synthetic aperture radar target recognition
Target recognition requires the ability to distinguish targets from non-targets, a capability called one-class generalization. To function as a one-class classifier, a neural network must have three types of generalization: within-class, between-class, and out-of-class. We discuss these three types of generalization and identify neural network architectures that meet these requirements. We have applied our one-class classifier ideas to the problem of automatic target recognition in synthetic aperture radar. We have compared three neural network algorithms: Carpenter and Grossberg's algorithmic version of the Adaptive Resonance Theory (ART-2A), Kohonen's Learning Vector Quantization (LVQ), and Reilly and Cooper's Restricted Columb Energy network (RCE). The ART 2-A neural network has given the best results, with 100% within-class, and out-of-class generalization. Experiments show that the network's performance is sensitive to vigilance and number of training set presentations.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 7137851
- Report Number(s):
- SAND-92-2104C; CONF-930355-1; ON: DE93000716
- Resource Relation:
- Conference: Institute of Electrical and Electronic Engineers (IEEE) international conference on neural networks, San Francisco, CA (United States), 28 Mar - 1 Apr 1993
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NEURAL NETWORKS
PATTERN RECOGNITION
RADAR
AUTOMATION
TARGETS
MEASURING INSTRUMENTS
RANGE FINDERS
450000* - Military Technology
Weaponry
& National Defense
990200 - Mathematics & Computers