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Proceedings Paper

Adaptive time-frequency classification of acoustic backscatter
Author(s): Brian A. Telfer; Harold H. Szu; Gerald J. Dobeck
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Paper Abstract

An adaptive time-frequency classifier algorithm is detailed and tested on a data set of acoustic backscatter from a metallic manmade object and natural clutter with synthetic reverberation noise. The algorithm is improved over previous versions in that it operates directly on time signals rather than their wavelet transforms, and in that the features measure time-frequency energy and are insensitive to phase differences (due to signal variations). The time-frequency features are initialized by selecting wavelet transform coefficients with the highest Fisher ratios. Similarities and differences of this classification algorithm with matching pursuit, a representation algorithm, are discussed. This is done to show the need to treat classification differently than representation for realistic cases where class boundaries significantly overlap. The adaptive classifier is shown to have significantly higher detection rates than Fourier transform and wavelet transform methods, as compared by receiver operating characteristics.

Paper Details

Date Published: 6 April 1995
PDF: 10 pages
Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); doi: 10.1117/12.205411
Show Author Affiliations
Brian A. Telfer, Naval Surface Warfare Ctr. (United States)
Harold H. Szu, Naval Surface Warfare Ctr. (United States)
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 2491:
Wavelet Applications II
Harold H. Szu, Editor(s)

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