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 machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of ‘normal’ natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier.