Feature discovery via neural networks for object recognition in SAR imagery
- Sandia National Labs., Albuquerque, NM (United States)
- New Mexico Univ., Albuquerque, NM (United States). Dept. of Electrical and Computer Engineering
A two-stage self-organizing neural network architecture has been applied to object recognition in Synthetic Aperture Radar imagery. The first stage performs feature extraction and implements a two- layer Neocognitron. The resulting feature vectors are presented to the second stage, an ART 2-A classifier network, which clusters the features into multiple target categories. Training is performed off-line in two steps. First, the Neocognitron self-organizes in response to repeated presentations of an object to recognize. During this training process, discovered features and the mechanisms for their extraction are captured in the excitatory weight patterns. In the second step, Necognitron learning is inhibited and the ART 2-A classifier forms categories in response to the feature vectors generated by additional presentations of the object to recognize. Finally, all training is inhibited and the system tested against a variety of objects and background clutter. In this paper we report the results of our initial experiments. The architecture recognizes a simulated tank vehicle at arbitrary azimuthal orientations at a single depression angle while rejecting clutter and other object returns. The neural architecture has achieved excellent classification performance using 20 clusters. 14 refs.
- Research Organization:
- Sandia National Labs., Albuquerque, NM (United States)
- Sponsoring Organization:
- DOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC04-76DP00789
- OSTI ID:
- 5727446
- Report Number(s):
- SAND-92-0134C; CONF-9206102--1; ON: DE92007046
- Country of Publication:
- United States
- Language:
- English
Similar Records
Feature discovery on segmented objects in SAR imagery using self-organizing neural networks
Feature discovery on segmented objects in SAR imagery using self-organizing neural networks
Related Subjects
420200* -- Engineering-- Facilities
Equipment
& Techniques
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
AERIAL SURVEYING
COMPUTERIZED SIMULATION
IMAGE PROCESSING
MEASURING INSTRUMENTS
NEURAL NETWORKS
PATTERN RECOGNITION
PROCESSING
RADAR
RANGE FINDERS
SIMULATION
SYNTHETIC-APERTURE RADAR