Recent processing string and fusion algorithm improvements for automated sea mine classification in shallow water

Tom Aridgides; Manuel F. Fernandez; Gerald J. Dobeck

doi:10.1117/12.487775

11 September 2003 Recent processing string and fusion algorithm improvements for automated sea mine classification in shallow water

Tom Aridgides, Manuel F. Fernandez, Gerald J. Dobeck

Author Affiliations +

Proceedings Volume 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII; (2003) https://doi.org/10.1117/12.487775
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

A novel sea mine computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The overall CAD/CAC processing string consists of pre-processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, feature orthogonalization, optimal subset feature selection, classification and fusion processing blocks. The range-dimension ACF is matched both to average highlight and shadow information, while also adaptively suppressing background clutter. For each detected object, features are extracted and processed through an orthogonalization transformation, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule, in the orthogonal feature space domain. The classified objects of 4 distinct processing strings are fused using the classification confidence values as features and logic-based, “M-out-of-N”, or LLRT-based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new shallow water high-resolution sonar imagery data. The processing string detection and classification parameters were tuned and the string classification performance was optimized, by appropriately selecting a subset of the original feature set. A significant improvement was made to the CAD/CAC processing string by utilizing a repeated application of the subset feature selection / LLRT classification blocks. It was shown that LLRT-based fusion algorithms outperform the logic based and the “M-out-of-N” ones. The LLRT-based fusion of the CAD/CAC processing strings resulted in up to a nine-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results, while maintaining a constant correct mine classification probability.

Citation Download Citation

Tom Aridgides, Manuel F. Fernandez, and Gerald J. Dobeck "Recent processing string and fusion algorithm improvements for automated sea mine classification in shallow water", Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003); https://doi.org/10.1117/12.487775

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