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Evaluation of image features for discriminating targets from false positives in synthetic aperture sonar imagery
Author(s): Jeffrey Dale; Aquila Galusha; James Keller; Alina Zare
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Paper Abstract

With the increasing popularity of using autonomous underwater vehicles (AUVs) to gather large quantities of Synthetic Aperture Sonar (SAS) seafloor imagery, the burden on human operators to identify targets in these seafloor images has increased significantly. Existing methods of automated target detection can have perfect or near-perfect accuracy, but often produce a high ratio of false positives. Thus, it is desired to find features that discriminate between targets and high-confidence false alarms. In this paper, we examine the potential of several classical methods of feature extraction in how well their generated features can separate the two classes of image tiles: those containing targets from those containing no targets. To quantify the ability of a set of features to separate these classes, we measure the region-based cross validation accuracy of a linear SVM trained on the features in question, extracted from SAS imagery provided to us by the U.S. Navy. We show that these general feature extraction methods show potential in the ATR problem, suggesting further research is warranted.

Paper Details

Date Published: 10 May 2019
PDF: 17 pages
Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 110120A (10 May 2019); doi: 10.1117/12.2519486
Show Author Affiliations
Jeffrey Dale, Univ. of Missouri (United States)
Aquila Galusha, Univ. of Missouri (United States)
James Keller, Univ. of Missouri (United States)
Alina Zare, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 11012:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV
Steven S. Bishop; Jason C. Isaacs, Editor(s)

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