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This paper presents a model-based approach to perform underwater target classification. Very high resolution imaging sonar has increased the opportunities to use highlight information contained in the target acoustic signature whereas underwater target classification is still mainly based on the analysis of geometrical properties of the acoustic shadows. Supervised classifiers generally use experimental or simulated samples of target acoustic signature in the training stage but when the testing set is different from the training set the performance can be altered. Here the classification method consists in comparing the A-scan of the detected target with a set of simulated A-scans generated by our Sonar Image Simulator (SIS) in the same operational conditions. The used simulator relies on acoustical ray tracing techniques and takes into account complicated underwater physical process to simulate an accurate time response of underwater targets (A-scan). Practically the classifier is made of a cascade of matched filters. Each is built by simulating the A-scan for a given object in a given orientation (and /or for a given size). The resulting scores can be used to rank likelihood of belonging to object classes. The result is flexible and gives a percentage match for each class. With this approach the training set can be extended increasingly to improve classification when classes are strongly correlated. This classification process is assessed on a few real sidescan sonar data. These first results are finally discussed and further work is deduced to improve the general classification task.

Model based classification of mine-like objects in sidescan sonar using the highlight information