Underwater UXO classification using Matched Subspace Classifier with synthetic sparse dictionaries

This paper is concerned with the development of a system for the classification of military munitions and unexploded ordnance (UXO) in shallow underwater environments. A Matched Subspace Classifier (MSC) is used in conjunction with Acoustic Color (AC) features generated from the raw sonar returns for munition characterization. Our classification hypothesis is that spectral content of the sonar backscatter display unique acoustic signatures providing good discrimination between different classes of detected contacts. The system is exclusively trained using synthetic sonar data and then tested using real data sets collected from a side-looking sonar system. These data sets were collected using underwater objects in relatively controlled and clutter-free environments. Classification results are presented using standard performance metrics such as probability of correct classification (P _CC ), probability of false alarm (P _FA ) in Receiver Operating Characteristic (ROC) curves, and confusion matrices.