Abstract
Target recognition in sonar imagery has long been an active research area in the maritime domain, especially in the mine-counter measure context. Recently it has received even more attention as new sensors with increased resolution have been developed; new threats to critical maritime assets and a new paradigm for target recognition based on autonomous platforms have emerged. With the recent introduction of Synthetic Aperture Sonar systems and high-frequency sonars, sonar resolution has dramatically increased and noise levels decreased. Sonar images are distance images but at high resolution they tend to appear visually as optical images. Traditionally algorithms have been developed specifically for imaging sonars because of their limited resolution and high noise levels. With high-resolution sonars, algorithms developed in the image processing field for natural images become applicable. However, the lack of large datasets has hampered the development of such algorithms. Here we present a fast and realistic sonar simulator enabling development and evaluation of such algorithms. We develop a classifier and then analyse its performances using our simulated synthetic sonar images. Finally, we discuss sensor resolution requirements to achieve effective classification of various targets and demonstrate that with high resolution sonars target highlight analysis is the key for target recognition.
Highlights
Target recognition in sonar imagery has long been an active research area in the maritime domain
We note that more information should be exploitable from the target’s highlight
We study here the quantity of information contained into the shape of the shadow, and how this information is retrievable depending on the pixel resolution
Summary
Target recognition in sonar imagery has long been an active research area in the maritime domain. Traditional dedicated ships are being replaced by small, low cost, autonomous platforms deployable by any vessel of opportunity. This creates new sensing and processing challenges, as the classification algorithms need to be fully automatic and run in real time on the platforms. The platforms’ behaviours require to be autonomously adapted online, to guarantee appropriate detection performance is met, sometimes on very challenging terrains This creates a direct link between sensing and mission planning, sometimes called active perception, where the data acquisition is directly controlled by the scene interpretation
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