An advanced, automatic, adaptive clutter suppression, sea mine detection, classification and fusion processing string has been developed and tested with high resolution sonar imagery dat. The overall computer-aided-detection/computer- aided-classification (CAD/CAC) string includes pre- processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, feature orthogonalization, subset feature selection, classification and fusion processing blocks. The ACF is an adaptive linear FIR filter, optimal in the Least Squares (LS) sense, and is applied to low-resolution data. Data pre-normalization, clipping and mean subtraction, allows application of a range dimension only ACF that is matched both to average highlight and shadow information, while simultaneously suppressing background clutter. Following post-ACF normalization, and detection consists of thresholding, clustering of exceedances and limiting the number of detections. Subsequently, features are extracted from high-resolution data and an orthogonalization transformation is applied to the features, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. Finally, the classified objects of three processing strings, developed by three different researchers, are fused, using an LLRT-based fusion rule. Processing string improvements have been developed over previous CAD/CAC and fusion string versions. The utility of the overall processing strings and their fusion was demonstrated with very shallow water high-resolution sonar imagery data sets, form a difficult environment. The processing string classification performance was optimized by appropriately selecting a subset of the original feature set. The fusion of the CAD/CAC processing strings resulted in improved mine classification capability, providing a three-fold false alarm rate reduction, compared to the best individual CAD/CAC processing string results.