Undersea target classification using canonical correlation analysis Pezeshki, Ali ; Azimi-Sadjadi, Mahmood R. ; Scharf, Louis L. "This work was supported by the U.S. Office of Naval Research (ONR) under Contracts N00014-02-1-0006 and N00014-04-1-0084." Canonical 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. Colorado State University. Libraries 2007 text ; image application/pdf ECEmra00064.pdf FACFECEN100544ARTI eng c2007 IEEE
Undersea target classification using canonical correlation analysis
Pezeshki, Ali ; Azimi-Sadjadi, Mahmood R. ; Scharf, Louis L.
"This work was supported by the U.S. Office of Naval Research (ONR) under Contracts N00014-02-1-0006 and N00014-04-1-0084."
Canonical 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.
Colorado State University. Libraries
2007
text ; image
application/pdf
ECEmra00064.pdf
FACFECEN100544ARTI
eng
c2007 IEEE