Vehicle Layover Removal in Circular SAR Images via ROSL

Abstract

Circular synthetic aperture radar (CSAR) has raised interest in both wide-aspect-angle 2-D imaging and 3-D feature reconstruction. As for CSAR 2-D imaging with a 360° aperture, vehicles spotlighted in the scene are depicted with multiview layover, which makes the imagery intuitively less comprehensible. In addition, the shape of the layover bulge depends on the elevation of the radar platform. Thus, otherwise identical vehicle targets appear differently in the image when the elevation deviates from a constant, which makes target discrimination more difficult. In this letter, subaperture images are vectorized and stacked to build a composite matrix. Decomposing the composite matrix via robust orthonormal subspace learning results in a low-rank matrix and a sparse matrix. The layover belongs to the sparse matrix and thus can be get rid of. The performance of the proposed method has been verified on synthesized and real CSAR data sets. Experimental results show that the multiview layover of vehicles is eliminated effectively. Moreover, the CSAR images become insensitive to elevation variation after layover removal, which benefits target discrimination.