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Exploiting the sparsity of edge information in synthetic aperture radar imagery for speckle reduction
Author(s): Theresa Scarnati; Edmund Zelnio; Christopher Paulson
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Paper Abstract

Synthetic aperture radar (SAR) images are corrupted with speckle noise, which manifests as a multiplicative gamma noise and reduces the contrast in imagery, making detection and classifi- cation using SAR images a difficult task. Many speckle reduction techniques aim to reduce this noise without including available prior knowledge about the speckle and the scene contents. In this investigation, we develop a new technique for speckle reduction which incorporates both the statistical model of speckle and the a priori knowledge about the sparsity of edges present in the scene. Using the proposed technique, we despeckle a synthetic image, a SAR image from the MSTAR data set and a SAR image from the Gotcha data set. Our results show that, with our method, we are able to visually improve the quality of SAR images. We show quantitatively that we are able to reduce speckle in homogeneous areas beyond comparable methods, while maintaining edge and target intensity information.

Paper Details

Date Published: 28 April 2017
PDF: 13 pages
Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 102010C (28 April 2017); doi: 10.1117/12.2267790
Show Author Affiliations
Theresa Scarnati, Arizona State Univ. (United States)
Edmund Zelnio, Air Force Research Lab. (United States)
Christopher Paulson, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10201:
Algorithms for Synthetic Aperture Radar Imagery XXIV
Edmund Zelnio; Frederick D. Garber, Editor(s)

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