Geodesic shape distance and integral invariant shape features for automatic target recognition

Scale, rotational, and translational invariance is important in shape classification problems for automatic target recognition. In this work, we employ integral invariant shape metrics and geodesic shape distance features for shape analysis of closed curves extracted from 2-D synthetic aperture sonar imagery. Results demonstrate that both metrics allow for good class separation over multiple target shapes whether through pair-wise comparison or with a small library of shape templates.