Fusion of sea mine detection and classification processing strings for sonar imagery

Abstract

An advanced, automatic, adaptive clutter suppression, sea mine detection, classification and fusion processing string has been developed and tested with new sonar imagery data. The overall CAD/CAC string includes pre-processing, adaptive clutter filtering (ACF), normalization, detection , features 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 input 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 3 different research teams, are fused, using a variety of fusion rules, including logic-based and a novel orthogonal LLRT-base done. The utility of the overall processing string and their fusion was demonstrated with high-resolution side-scan sonar imagery from a difficult shallow water environment. The processing string classification performance was optimized by appropriately selecting a subset of the original feature set. The overall CAD/CAC processing string fusion result in improved mine classification capability, providing up to a four-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results.