Journal article
Sonar target representation using two-dimensional Gabor wavelet features
The Journal of the Acoustical Society of America, Vol.148(4), pp.2061-2072
10/2020
DOI: 10.1121/10.0002168
PMID: 33138505
Abstract
This paper introduces a feature extraction technique that identifies highly informative features from sonar magnitude spectra for automated target classification. The approach involves creating feature representations through convolution of a two-dimensional Gabor wavelet and acoustic color magnitudes to capture elastic waves. This feature representation contains extracted localized features in the form of Gabor stripes, which are representative of unique targets and are invariant of target aspect angle. Further processing removes non-informative features through a threshold-based culling. This paper presents an approach that begins connecting model-based domain knowledge with machine learning techniques to allow interpretation of the extracted features while simultaneously enabling robust target classification. The relative performance of three supervised machine learning classifiers, specifically a support vector machine, random forest, and feed-forward neural network are used to quantitatively demonstrate the representations' informationally rich extracted features. Classifiers are trained and tested with acoustic color spectrograms and features extracted using the algorithm, interpreted as stripes, from two public domain field datasets. An increase in classification performance is generally seen, with the largest being a 47% increase from the random forest tree trained on the 1-31 kHz PondEx10 data, suggesting relatively small datasets can achieve high classification accuracy if model-cognizant feature extraction is utilized.
Details
- Title: Subtitle
- Sonar target representation using two-dimensional Gabor wavelet features
- Creators
- Bernice Kubicek - University of IowaAnanya Sen Gupta - University of IowaIvars Kirsteins - Naval Undersea Warfare Center
- Resource Type
- Journal article
- Publication Details
- The Journal of the Acoustical Society of America, Vol.148(4), pp.2061-2072
- DOI
- 10.1121/10.0002168
- PMID
- 33138505
- NLM abbreviation
- J Acoust Soc Am
- ISSN
- 0001-4966
- eISSN
- 1520-8524
- Publisher
- American Institute of Physics
- Grant note
- DOI: 10.13039/100000006, name: Office of Naval Research, award: N000014-19-1-2436; DOI: 10.13039/100000001, name: National Science Foundation, award: 1808463
- Language
- English
- Date published
- 10/2020
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197088302771
Metrics
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