An advanced, automatic, adaptive clutter suppression, sea mine detection-classification and fusion processing string has been developed and tested with sonar imagery data. The overall string includes pre-processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, classification and fusion processing blocks. The ACF is a multi-dimensional adaptive linear FIR filter, optimal in the Least Squares sense, and is applied to low- resolution data. It performs simultaneous background clutter suppression and preservation of an average peak target signature. Following 2D normalization, the 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 the LF and HF processing strings are fused. The utility of the overall processing string was demonstrated with two new shallow water high-resolution sonar imagery datasets. The processing string classification performance was optimized by appropriately selecting a subset of the original feature set. The overall ACF, detection, feature extraction and orthogonalization, LLRT- based classification and fusion processing string resulted in improved mine classification capability, providing a three-fold false alarm rate reduction, compared to previous results. A wide-sense stationary covariance model was utilized in the ACF algorithm design, significantly reducing the algorithm implementation complexity, and the implementation of the overall processing string in real-time was demonstrated.