Principal component analysis in the wavelet domain: new features for underwater object recognition

Gordon S. Okimoto; David W. Lemonds

doi:10.1117/12.357091

2 August 1999 Principal component analysis in the wavelet domain: new features for underwater object recognition

Gordon S. Okimoto, David W. Lemonds

Proceedings Volume 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV; (1999) https://doi.org/10.1117/12.357091
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Principal component analysis (PCA) in the wavelet domain provides powerful features for underwater object recognition applications. The multiresolution analysis of the Morlet wavelet transform (MWT) is used to pre-process echo returns from targets ensonified by biologically motivated broadband signal. PCA is then used to compress and denoise the resulting time-scale signal representation for presentation to a hierarchical neural network for object classification. Wavelet/PCA features combined with multi-aspect data fusion and neural networks have resulted in impressive underwater object recognition performance using backscatter data generated by simulate dolphin echolocation clicks and bat- like linear frequency modulated upsweeps. For example, wavelet/PCA features extracted from LFM echo returns have resulted in correct classification rates of 98.6 percent over a six target suite, which includes two mine simulators and four clutter objects. For the same data, ROC analysis of the two-class mine-like versus non-mine-like problem resulted in a probability of detection of 0.981 and a probability of false alarm of 0.032 at the 'optimal' operating point. The wavelet/PCA feature extraction algorithm is currently being implemented in VLSI for use in small, unmanned underwater vehicles designed for mine- hunting operations in shallow water environments.

Citation Download Citation

Gordon S. Okimoto and David W. Lemonds "Principal component analysis in the wavelet domain: new features for underwater object recognition", Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); https://doi.org/10.1117/12.357091

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