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In this paper we present a method for clustering and classification of acoustic color data based on statistical analysis of functions using square-root velocity functions (SVRF). The convenience of the SVRF is that it transforms the Fisher-Rao metric into the standard L2metric. As a result, a formal distance can be calculated using geodesic paths. Moreover, this method allows optimal deformations between acoustic color data to be computed for any two targets allowing for robustness to measurement error. Using the SVRF formulation statistical models can then be constructed using principal component analysis to model the functional variation of acoustic color data. Empirical results demonstrate the utility of functional data analysis for improving performance results in pattern recognition using acoustic color data.

The article investigates the use of Canonical Coordinate Analysis (CCA) for sonar target detection and classification. It is stated that CCA method has shown great promise in underwater target classification problems using sonar backscatter. The article also offers a review of the CCA method and its application as a feature extraction or detection tool for implementing the Neyman-Pearson detector.