An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been
developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their
expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the
overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery.
Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion
algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox
transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset
feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that
cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage
Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC
processing string results, and providing the capability to correctly call the majority of targets while maintaining a very
low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that
the algorithm yields similar performance with the training and test sets.