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Proceedings Paper

Enhanced ATR using Fisher fusion techniques with application to side-looking sonar
Author(s): Charles M. Ciany; William C. Zurawski
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Paper Abstract

Raytheon has extensively processed high-resolution sidescan sonar images with its Automatic Target Recognition (ATR) algorithms to classify target-like objects (TLOs) in a variety of underwater environments. The ATR algorithm segments the image into candidate highlight and shadow regions of interest (ROIs), and extracts and scores features from these ROIs. The TLOs are classified by thresholding an overall classification score, formed by summing the individual feature scores. The algorithm performs reliably against TLOs that exhibit highlight and shadow regions that are both distinct relative to the ambient background. However, the sonar images for many real-world undersea environments can contain a significant percentage of TLOs exhibiting either "weak" highlight or shadow regions. Robust performance in these environments is achieved by tailoring the individual feature scoring algorithms to optimize the separation between the corresponding highlight or shadow feature scores of targets and non-targets. This study examines modifications to a previously presented alternate approach that employs Fisher fusion principles to generate optimal weighting coefficients which are applied to the individual feature scores before final classification processing. Results from processing of at-sea data sets demonstrate the performance benefits obtained from the modifications.

Paper Details

Date Published: 29 April 2010
PDF: 10 pages
Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76640Z (29 April 2010); doi: 10.1117/12.852401
Show Author Affiliations
Charles M. Ciany, Raytheon Co. (United States)
William C. Zurawski, Raytheon Co. (United States)

Published in SPIE Proceedings Vol. 7664:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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