Robust morphological detection of sea mines in side-scan sonar images

Abstract

The automated detection of sea mines remains an increasingly important humanitarian and military task. In recent years, research efforts have been concentrated on developing algorithms that detect mines in complicated littoral environments. Acquired high-resolution side-looking sonar images are often heavily infested with artifacts from natural and man-made clutter. As a consequence, automated detection algorithms, designed for high probability of detection, suffer from a large number of false alarms. To remedy this situation, sophisticated feature extraction and pattern classification techniques are commonly used after detection. In this paper, we propose a nonlinear detection algorithm, based on mathematical morphology, for the robust detection of sea mines. The proposed algorithm is fast and performs well under a variety of sonar modalities and operating conditions. Our approach is based on enhancing potential mine signatures by extracting highlight peaks of appropriate shape and size and by boosting the amplitude of the peaks associated with a potential shadow prior to detection. Signal amplitudes over highlight peaks are extracted using a flat morphological top-hat by reconstruction operator. The contribution of a potential shadow to the detection image is incorporated by increasing the associated highlight amplitude by an amount proportional to the relative contrast between highlight and shadow signatures. The detection image is then thresholded at mid-gray level. The largest p targets from the resulting binary image are then labelled as potential targets. The number of false alarms in the detection image is subsequently reduced to an acceptable level by a feature extraction and classification module. The detection algorithm is tested on two side-scan sonar databases provided by the Coastal Systems Station, Panama City, Florida: SONAR-0 and SONAR-3.