In this paper we apply a sparse signal recovery technique for synthetic aperture radar (SAR) image formation from interrupted phase history data. Timeline constraints imposed on multi-function modern radars result in interrupted SAR data collection, which in turn leads to corrupted imagery that degrades reliable change detection. In this paper we extrapolate the missing data by applying the basis pursuit denoising algorithm (BPDN) in the image formation step, effectively, modeling the SAR scene as sparse. We investigate the effects of regular and random interruptions on the SAR point spread function (PSF), as well as on the quality of both coherent (CCD) and non-coherent (NCCD) change detection. We contrast the sparse reconstruction to the matched filter (MF) method, implemented via Fourier processing with missing data set to zero. To illustrate the capabilities of the gap-filling sparse reconstruction algorithm, we evaluate change detection performance using a pair of images from the GOTCHA data set.

Date Published: 2 May 2012
PDF: 16 pages
Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 839408 (2 May 2012);

Published in SPIE Proceedings Vol. 8394:
Algorithms for Synthetic Aperture Radar Imagery XIX
Edmund G. Zelnio; Frederick D. Garber, Editor(s)