An advanced, automatic, adaptive clutter suppression, pre- and post-detection level fusion, sea mine detection and classification processing string has been developed and tested with sonar imagery data. The overall string includes preprocessing, adaptive clutter filtering (ACF), normalization, detection, feature extraction and classification 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 (normalized shape of mine highlights -- computed a priori using training set data). After data alignment, using a 3-dimensional ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range- crossrange domain. Following 2-d normalization, the detection consists of thresholding, clustering of exceedances and limiting the number of detections. Finally, features are extracted from high resolution data and a orthogonalization transformation is applied to the features, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. Various fusion approaches were investigated including pre-detection fusion using the 3-d ACF string, post-detection fusion of the 2d ACF strings and an overall fusion of the two previous strings. The utility of the various processing strings was demonstrated with two new shallow water high resolution sonar imagery data sets. The overall ACF, pre- and post-detection level fusion, feature orthogonalization, LLRT-based classification processing string provided mine classification capability and false alarm rate performance exceeding the one of an expert sonar operator. A wide-sense stationary covariance model was utilized in the ACF algorithm design, significantly reducing the algorithm implementation complexity, thus enabling an easy implementation of the overall processing string in real-time.