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Proceedings Paper

Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction
Author(s): Theresa Scarnati; Anne Gelb
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Paper Abstract

In observing multiple synthetic aperture radar (SAR) images of the same scene, it is apparent that the brightness distributions of the images are not smooth, but rather composed of complicated granular patterns of bright and dark spots. Further, these brightness distributions vary from image to image. This salt and pepper like feature of SAR images, called speckle, reduces the contrast in the images and negatively affects texture based image analysis. This investigation uses the variance based joint sparsity reconstruction method for forming SAR images from the multiple SAR images. In addition to reducing speckle, the method has the advantage of being non-parametric, and can therefore be used in a variety of autonomous applications. Numerical examples include reconstructions of both simulated phase history data that result in speckled images as well as the images from the MSTAR T-72 database.

Paper Details

Date Published: 27 April 2018
PDF: 14 pages
Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470R (27 April 2018); doi: 10.1117/12.2500209
Show Author Affiliations
Theresa Scarnati, Arizona State Univ. (United States)
Air Force Research Lab. (United States)
Anne Gelb, Dartmouth College (United States)


Published in SPIE Proceedings Vol. 10647:
Algorithms for Synthetic Aperture Radar Imagery XXV
Edmund Zelnio; Frederick D. Garber, Editor(s)

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