Underwater target classification using multi-aspect fusion and neural networks

This paper presents an extension of the research work on the wavelet-based classification scheme developed to discriminate underwater mine-like from non-mine-like objects using the acoustic backscattered signals.(1) Based on the single-aspect classification results, the robustness and discriminatory power of the selected features, and the generalization ability of the trained network are demonstrated on several cases. To further improve the overall classification accuracy, the classification results of multiple aspect angles are fused together. Two different fusion approaches are considered and their performance is tested on ten different realizations. The final results show excellent classification accuracy of 96% for only a 4% false alarm rate.