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Title: Sonar target representation using two-dimensional Gabor wavelet features

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.

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Award ID(s):
1808463
PAR ID:
10539912
Author(s) / Creator(s):
; ;
Editor(s):
Michalopolou, Zoi-Heleni
Publisher / Repository:
Journal of Acoustical Society of America
Date Published:
Journal Name:
The Journal of the Acoustical Society of America
Volume:
148
Issue:
4
ISSN:
0001-4966
Page Range / eLocation ID:
2061 to 2072
Subject(s) / Keyword(s):
signal processing features information Note: This paper while topically different in the application (sonar) from the NSF project focus, acknowledged the NSF grant due to its early role in training the lead student author (Bernice Kubicek) in classic concepts in signal processing theory, data and information science, and especially understanding the role of signal peaks and signal features as carrying relevant information, which is broadly foundational to the work reported here.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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