10 June 2005 Further improvements in computer aided detection/computer aided classification (CAD/CAC) of bottom mines
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Abstract
In 1999 Raytheon adapted its shallow-water Side-Looking Sonar (SLS) CAD/CAC algorithm to process side-scan sonar data obtained with the Woods Hole Oceanographic Institute's Remote Environmental Monitoring Units (REMUS) autonomous underwater vehicle (AUV). To date, Raytheon has demonstrated the ability to effectively execute mine-hunting missions with the REMUS vehicle through the fusion of its CAD/CAC algorithm with several other CAD/CAC algorithms to achieve a high probability of correct classification while maintaining a low false alarm rate. Raytheon recently reported CAD/CAC algorithm enhancements that demonstrated a significant improvement in overall CAD/CAC performance across a diverse set of environments. Additional algorithm enhancements that further improve performance over this same set of environments are described herein. The paper also presents results obtained from processing this diverse environmental data set with the enhanced Raytheon CAD/CAC algorithm, and the performance achieved by fusing the Raytheon CAD/CAC outputs with those of the other CAD/CAC algorithms.
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Charles M. Ciany and William C. Zurawski "Further improvements in computer aided detection/computer aided classification (CAD/CAC) of bottom mines", Proc. SPIE 5794, Detection and Remediation Technologies for Mines and Minelike Targets X, (10 June 2005); doi: 10.1117/12.603937; https://doi.org/10.1117/12.603937
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