Position, Scale, and Rotation Invariant Target Recognition Using Range Imagery.
Abstract
This thesis explores a new approach to the recognition of tactical targets using a multifunction laser radar sensor. Targets of interest were tanks, jeeps, and trucks. Doppler images were segmented and overlaided onto a relative range image. The resultant shapes were then transformed into a position, scale, and rotation invariant (PSRI) feature space. The classification processes used the correlation peak of the template PSRI space and the target PSRI space as features. Two classification methods were implemented: a classical distance measurement approach and a new biologically-based neural network multilayer perception architecture. Both methods demonstrated classification rates near 100% with a true rotation invariance demonstrated up to 20 degrees. Neural networks were shown to have a distinct advantage in a robust environment and when a figure of merit criteria was applied. A space domain correlation was developed using local normalization and multistage processing to locate and classify targets in high clutter and with partially occluded targets.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1987
- Accession Number
- ADA188828
Entities
People
- Steven E. Troxel
Organizations
- Air Force Institute of Technology