Composite Classifiers for Automatic Target Recognition
Abstract
Composite classifiers that are constructed by combining a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers. We analyze a number of classifier fusion algorithms, which combine the outputs of all the component classifiers, and classifier selection algorithms, which use a cascade architecture that relies on a subset of the component classifiers. Each composite classifier is implemented and tested on a large data set of real FLIR images. The performances of the proposed composite classifiers are compared based on their classification ability and computational complexity. We demonstrate that the composite classifier based on a cascade architecture greatly reduces computational complexity, with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 1998
- Accession Number
- ADA356492
Entities
People
- Lin-cheng Wang
- Nasser M. Nasrabadi
- Sandor Der
Organizations
- United States Army Research Laboratory