Title :
Comparison of different classification algorithms for underwater target discrimination
Author :
Li, Donghui ; Azimi-Sadjadi, Mahmood R. ; Robinson, Marc
Author_Institution :
Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA
Abstract :
Classification of underwater targets from the acoustic backscattered signals is considered. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.
Keywords :
acoustic signal processing; neural nets; pattern classification; signal classification; support vector machines; K-nearest neighbor classifier; SVMs; acoustic backscattered signals; classification algorithms; probabilistic neural networks; receiver operating characteristic; support vector machines; underwater target classification; underwater target discrimination; wideband 80-kHz acoustic backscattered data set; Acoustic testing; Benchmark testing; Cities and towns; Classification algorithms; Neural networks; Sea measurements; Support vector machine classification; Support vector machines; Underwater acoustics; Wideband; Acoustic Stimulation; Algorithms; Discrimination (Psychology); Normal Distribution;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2003.820621
