Application of Fisher fusion techniques to improve the individual performance of sonar computer-aided detection/computer-aided classification (CAD/CAC) algorithms

Charles M. Ciany; William C. Zurawski

doi:10.1117/12.819419

4 May 2009 Application of Fisher fusion techniques to improve the individual performance of sonar computer-aided detection/computer-aided classification (CAD/CAC) algorithms

Charles M. Ciany, William C. Zurawski

Author Affiliations +

Proceedings Volume 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV; 73030F (2009) https://doi.org/10.1117/12.819419
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Raytheon has extensively processed high-resolution sidescan sonar images with its CAD/CAC algorithms to provide classification of targets in a variety of shallow underwater environments. The Raytheon CAD/CAC algorithm is based on non-linear image segmentation into highlight, shadow, and background regions, followed by extraction, association, and scoring of features from candidate highlight and shadow regions of interest (ROIs). The targets are classified by thresholding an overall classification score, which is formed by summing the individual feature scores. The algorithm performance is measured in terms of probability of correct classification as a function of false alarm rate, and is determined by both the choice of classification features and the manner in which the classifier rates and combines these features to form its overall score. In general, the algorithm performs very reliably against targets that exhibit "strong" highlight and shadow regions in the sonar image- i.e., both the highlight echo and its associated shadow region from the target are distinct relative to the ambient background. However, many real-world undersea environments can produce sonar images in which a significant percentage of the targets exhibit either "weak" highlight or shadow regions in the sonar image. The challenge of achieving robust performance in these environments has traditionally been addressed by modifying 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 an alternate approach that employs principles of Fisher fusion to determine a set of optimal weighting coefficients that are applied to the individual feature scores before summing to form the overall classification score. The results demonstrate improved performance of the CAD/CAC algorithm on at-sea data sets.

Citation Download Citation

Charles M. Ciany and William C. Zurawski "Application of Fisher fusion techniques to improve the individual performance of sonar computer-aided detection/computer-aided classification (CAD/CAC) algorithms", Proc. SPIE 7303, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIV, 73030F (4 May 2009); https://doi.org/10.1117/12.819419

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