Buried underwater target classification using the new BOSS and canonical correlation decomposition feature extraction

Multi-aspect detection and classification of buried underwater objects using the new Buried Object Scanning Sonar (BOSS) data is the main goal of this project. Canonical coordinate decomposition (CCD) was applied to extract the most coherent features of the buried or bottom objects in two sonar pings with a certain separation. CCD provides an elegant framework for analyzing linear dependence and mutual information between two data channels. These features are then used for subsequent classification. For this study, single-aspect and multi-aspect classification schemes are evaluated, and the results presented in terms of confusion matrices. Additionally, the results of applying both the single and multi-aspect classifiers to the entire test runs are presented to show the real usefulness of the algorithms for buried/bottom mine-hunting.