Abstract
A generalized quadratic (Bayesian-like) classification system has been developed for evaluating the performance of other classifiers, such as neural networks, in automatic target recognition (ATR). The system was tested using multispectral real data as well as computer generated data sets. The classifier employs the covariance matrix and centroid of the feature set to describe each region. The system then calculates the likelihood associated with an unknown object belonging to a defined region. A multivariate normal distribution is assumed in calculating this likelihood. The system utilizes a learning algorithm to continuously upgrade performance and has shown near 100 percent accuracy even after very short training periods.
| Original language | English |
|---|---|
| Pages (from-to) | 537-548 |
| Number of pages | 12 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 1349 |
| State | Published - 1990 |
| Event | Applications of Digital Image Processing XIII - San Diego, CA, USA Duration: Jul 10 1990 → Jul 13 1990 |