In this paper we consider classification of civilian vehicles using circular synthetic aperture radar. For wide-field application in which the scene radius is a significant fraction of the flight path radius, vehicle signatures are spatially variant due to layover. For a ten-class identification task using simulated X-band signatures, we demonstrate 96% correct classification for single-pass 2D imagery with scene radius 0.4 times the flight radius. Simulated scattering data include multi-path and material effects. Image signatures are represented by sets of attributed scattering centers. Dissimilarity between attributed point sets is computed via a minimized partial Hausdorff distance. Using multidimensional scaling, the distances are represented in a low-dimensional Euclidean space for both visualization and improved classification. The minimized partial Hausdorff distance, while not a true distance, empirically shows remarkable fidelity to the triangle inequality. Finally, in a limited two-class study, we show that three-dimensional imaging of layover points using polarization cues provides improved class separability.

Date Published: 28 April 2009
PDF: 11 pages
Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370R (28 April 2009); doi: 10.1117/12.823462

Published in SPIE Proceedings Vol. 7337:
Algorithms for Synthetic Aperture Radar Imagery XVI
Edmund G. Zelnio; Frederick D. Garber, Editor(s)