Scharf, Louis L., authorAzimi-Sadjadi, Mahmood R., authorPezeshki, Ali, authorIEEE, publisher2007-01-032007-01-032007Pezeshki, Ali, Mahmood R. Azimi-Sadjadi, and Louis L. Scharf, Undersea Target Classification Using Canonical Correlation Analysis, IEEE Journal of Oceanic Engineering 32, no. 4 (October 2007): 948-955.http://hdl.handle.net/10217/994Canonical correlation analysis is employed as a multiaspect feature extraction method for underwater target classification. The method exploits linear dependence or coherence between two consecutive sonar returns, at different aspect angles. This is accomplished by extracting the dominant canonical correlations between the two sonar returns and using them as features for classifying mine-like objects from nonmine-like objects. The experimental results on a wideband acoustic backscattered data set, which contains sonar returns from several mine-like and nonmine-like objects in two different environmental conditions, show the promise of canonical correlation features for mine-like versus nonmine-like discrimination. The results also reveal that in a fixed bottom condition, canonical correlation features are relatively invariant to changes in aspect angle.born digitalarticleseng©2007 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.underwater target classificationmultiaspect feature extractionlinear dependence and coherencecanonical correlationsText