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Proceedings Paper

Texture-based discrimination of man-made and natural objects in sidescan sonar imagery
Author(s): Ronald T. Kessel
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Paper Abstract

High-resolution sidescan sonars are often used in underwater warfare for large-area surveys of the seafloor in the search for sea mines. Much effort has gone toward the automatic detection of sea mines. In its more advanced forms, such auto-detection entails pattern recognition: the automatic assignment of class labels (target/non-target) to signatures according to their distinctive features. This paper demonstrates a texture-based feature for automatically discriminating between man-made and natural objects. Real sonar data is used, and the demonstration includes performance estimates in the form of the receiver-operator characteristic (ROC) curves necessary (though often omitted) for evaluating detectors for operational use. The merits of redefining the allowable automatic responses-from the classes of mine targets ultimately sought, to the class of man-made objects more generally-are reviewed from both the pattern-recognition and operational perspectives.

Paper Details

Date Published: 25 August 2003
PDF: 9 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487321
Show Author Affiliations
Ronald T. Kessel, Defence Research and Development Canada (Canada)

Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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