One-class classifier networks for target recognition applications
Target recognition requires the ability to distinguish targets from non-targets, a capability called one-class generalization. Many neural network pattern classifiers fail as one-class classifiers because they use open decision boundaries. To function as 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 Coulomb Energy network (RCE). The ART 2-A neural network gives the best results, with 100% within-class, between-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:
- DOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 6755553
- Report Number(s):
- SAND-93-0084C; ON: DE93006288
- Country of Publication:
- United States
- Language:
- English
Similar Records
One-class classifiers and their application to synthetic aperture radar target recognition
One-class classifier networks for target recognition applications
Related Subjects
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE
450000* -- Military Technology
Weaponry
& National Defense
47 OTHER INSTRUMENTATION
ALGORITHMS
CLASSIFICATION
DECISION MAKING
MATHEMATICAL LOGIC
MEASURING INSTRUMENTS
NEURAL NETWORKS
PATTERN RECOGNITION
RADAR
RANGE FINDERS
SORTING
SYNTHETIC-APERTURE RADAR
TARGETS