Abstract
In this paper, a new unsupervised algorithm for the detection of underwater targets in synthetic aperture sonar (SAS) imagery is proposed. The method capitalizes on the high-quality SAS imagery whose high resolution permits many pixels on target. One particularly novel component of the method also detects sand ripples and estimates their orientation. The overall algorithm is made fast by employing a cascaded architecture and by exploiting integral-image representations. As a result, the approach makes near-real-time detection of proud targets in sonar data onboard an autonomous underwater vehicle (AUV) feasible. No training data are required because the proposed method is adaptively tailored to the environmental characteristics of the sensed data that are collected in situ. To validate and assess the performance of the proposed detection algorithm, a large-scale study of SAS images containing various mine-like targets is undertaken. The data were collected with the MUSCLE AUV during six large sea experiments, conducted between 2008 and 2012, in different geographical locations with diverse environmental conditions. The analysis examines detection performance as a function of target type, aspect, range, image quality, seabed environment, and geographical site. To our knowledge, this study-based on nearly 30 000 SAS images collectively covering approximately 160 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of seabed, and involving over 1100 target detection opportunities-represents the most extensive such systematic, quantitative assessment of target detection performance with SAS data to date. The analysis reveals the variables that have the largest impact on target detection performance, namely, image quality and environmental conditions on the seafloor. Ways to exploit the results for adaptive AUV surveys using through-the-sensor data are also suggested.
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