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Proceedings Paper

Sparse and accurate high resolution SAR imaging
Author(s): Duc Vu; Kexin Zhao; William Rowe; Jian Li
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Paper Abstract

We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA has been shown to reconstruct SAR images with excellent side lobes suppression and high resolution enhancement. We first reconstruct the SAR images using IAA, and then we enforce sparsity by using MAP with a sparsity inducing prior. By coupling these two methods, we can produce a sparse and accurate high resolution image that are conducive for feature extractions and target classification applications. In addition, we show how IAA can be made computationally efficient without sacrificing accuracies, a desirable property for SAR applications where the size of the problems is quite large. We demonstrate the success of our approach using the Air Force Research Lab's "Gotcha Volumetric SAR Data Set Version 1.0" challenge dataset. Via the widely used FFT, individual vehicles contained in the scene are barely recognizable due to the poor resolution and high side lobe nature of FFT. However with our approach clear edges, boundaries, and textures of the vehicles are obtained.

Paper Details

Date Published: 2 May 2012
PDF: 8 pages
Proc. SPIE 8394, Algorithms for Synthetic Aperture Radar Imagery XIX, 839407 (2 May 2012); doi: 10.1117/12.918812
Show Author Affiliations
Duc Vu, Univ. of Florida (United States)
Kexin Zhao, Univ. of Florida (United States)
William Rowe, Univ. of Florida (United States)
Jian Li, Univ. of Florida (United States)

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

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