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Proceedings Paper

Enhancement of multi-pass 3D circular SAR images using sparse reconstruction techniques
Author(s): Matthew Ferrara; Julie Ann Jackson; Christian Austin
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Paper Abstract

This paper demonstrates image enhancement for wide-angle, multi-pass three-dimensional SAR applications. Without sufficient regularization, three-dimensional sparse-aperture imaging from realistic data-collection scenarios results in poor quality, low-resolution images. Sparsity-based image enhancement techniques may be used to resolve high-amplitude features in limited aspects of multi-pass imagery. Fusion of the enhanced images across multiple aspects in an approximate GLRT scheme results in a more informative view of the target. In this paper, we apply two sparse reconstruction techniques to measured data of a calibration top-hat and of a civilian vehicle observed in the AFRL publicly-released 2006 Circular SAR data set. First, we employ prominent-point autofocus in order to compensate for unknown platform motion and phase errors across multiple radar passes. Each sub-aperture of the autofocused phase history is digitally-spotlighted (spatially low-pass filtered) to eliminate contributions to the data due to features outside the region of interest, and then imaged with l1-regularized least squares and CoSaMP. The resulting sparse sub-aperture images are non-coherently combined to obtain a wide-angle, enhanced view of the target.

Paper Details

Date Published: 29 April 2009
PDF: 10 pages
Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 733702 (29 April 2009); doi: 10.1117/12.820256
Show Author Affiliations
Matthew Ferrara, Air Force Research Lab. (United States)
Julie Ann Jackson, The Ohio State Univ. (United States)
Christian Austin, The Ohio State Univ. (United States)


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

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