Fusion of adaptive algorithms for the classification of sea mines using high resolution side scan sonar in very shallow water

A new sea mine computer-aided-detection/computer-aided-classification (CAD/CAC) processing string has been developed. The CAD/CAC processing string consists of preprocessing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, classification and fusion processing blocks. The range-dimension ACF is an adaptive linear FIR filter, which is matched both to average highlight and shadow information, while also simultaneously 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 3 distinct processing strings, developed by 3 different researchers, are fused, using the classification confidence values as features and logic-based, M-out-of-N, or novel 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. It was shown that LLRT-based fusion algorithms outperform the logic-based or the M-out-of-N ones. The LLRT-based fusion of the CAD/CAC processing strings resulted in a four-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results, while maintaining a constant correct mine classification probability.