Textural lacunarity for semi-supervised detection in sonar imagery

Wavelet energy-based lacunarity features, which measure deviations from translational statistical invariance over multiple scales, were recently proposed for object detection and classification in sonar imagery. The authors here extend the idea to incorporate further robustness to background type whilst retaining sensitivity to local changes in texture caused by the presence of man-made objects. The resulting textural-lacunarity features are constructed by estimating the joint distribution of local neighbourhoods with empirical distributions over an adaptive texton dictionary. Experiments on a synthetic aperture sonar imagery dataset suggest that the features offer significant improvements in the receiver operating curve.