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 3 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 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. A discussion is presented illustrating the statistical independence of the CAD/CAC processing string outputs and providing insights as to the processing gains to be expected with fusion. 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 up to 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.