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31 May 1996 Automated recognition of acoustic-image clutter
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The shallow water coastal region is a particularly challenging environment in which to perform automated detection of mines. Sonar images taken in this region contain several types of acoustic image clutter including physical bottom clutter, high reverberation, multipath returns, and artifacts of sonar image construction such as high gain levels. The existence of image clutter is the primary difficulty in performing effective automated detection because this clutter typically results in a high false target rate. The identification and elimination of false targets is therefore required for effective automated target detection. A clutter recognition algorithm has been developed that has been very effective in reducing the false target rate for a set of side look sonars images. This algorithm acts as a supplement to detection and classification algorithms that identify and produce features spaces for possible minelike objects located in the images. The clutter recognition algorithm works by grouping detected objects by location and distance into image subspaces. Sets of group features are then calculated and the values of these features used to determine if each object is an actual mine or is part of an extended clutter formation. When used in conjunction with a statistics based detector and a fractal based target classifier, the clutter recognition algorithm produced an average false target rate of less than two false targets per image even though some of the more cluttered images contained over a hundred detected objects. Also, when used as an adjunct to a neural network classifier, the algorithm reduced the false target rate achieved by the network.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Susan R. Nelson and Susan M. Tuovila "Automated recognition of acoustic-image clutter", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996);

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