Journal
IET SIGNAL PROCESSING
Volume 6, Issue 5, Pages 484-493Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-spr.2011.0070
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Funding
- EPSRC [EP/H012834/1]
- UK Ministry of Defence, as part of the University Defence Research Centre on Signal Processing
- Engineering and Physical Sciences Research Council [EP/H012834/1] Funding Source: researchfish
- EPSRC [EP/H012834/1] Funding Source: UKRI
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An anomaly detection approach is considered for the mine hunting in sonar imagery problem. The authors 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.
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