Detailed investigation of cascaded Volterra fusion of processing strings for automated sea mine classification in very shallow water

Tom Aridgides; Manuel Fernández

doi:10.1117/12.663768

18 May 2006 Detailed investigation of cascaded Volterra fusion of processing strings for automated sea mine classification in very shallow water

Tom Aridgides, Manuel Fernández

Author Affiliations +

Proceedings Volume 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI; 62171C (2006) https://doi.org/10.1117/12.663768
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

An improved 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, subimage adaptive clutter filtering (SACF), normalization, detection, feature extraction, repeated application of optimal subset feature selection, feature orthogonalization and log-likelihood-ratio-test (LLRT) classification processing, and fusion processing blocks. The classified objects of 3 distinct processing strings are fused using the classification confidence values as features and either "M-out-of-N" or LLRT-based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new very 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. Two significant fusion algorithm improvements were made. First, a new nonlinear (Volterra) feature LLRT fusion algorithm was developed. Second, a repeated application of the subset Volterra feature selection/feature orthogonalization/LLRT fusion block was utilized. It was shown that this cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms the "M-out- of-N," the baseline LLRT and single-stage Volterra feature LLRT fusion algorithms, and also yields an improvement over the best single CAD/CAC processing string, providing a significant reduction in the false alarm rate. Additionally, the robustness of cascade Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

Citation Download Citation

Tom Aridgides and Manuel Fernández "Detailed investigation of cascaded Volterra fusion of processing strings for automated sea mine classification in very shallow water", Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62171C (18 May 2006); https://doi.org/10.1117/12.663768

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