Fractal dimension based sand ripple suppression for mine hunting with sidescan sonar
نویسندگان
چکیده
Sand ripples present a difficult challenge to current mine hunting approaches. We propose a robust and adaptive method that suppresses sand ripples prior to the detection stage. The method exploits a fractal model of the seabed and the connection between: dual-tree wavelets and local, directional fractal dimension; interscale energy ratios, scale invariant frequency localised fractal dimension, and a novel wavelet shrinkage approach. Tests on a reasonably large, real synthetic aperture sonar imagery dataset show that the ripple suppression method preserves detection performance of the matched filter on nonrippled data and significantly increases the detection performance on data that contain ripples.
منابع مشابه
Fractal dimension, wavelet shrinkage, and anomaly detection for mine hunting
An anomaly detection approach is considered for the mine hunting in sonar imagery problem. We exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machi...
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