Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction

Theresa Scarnati; Anne Gelb

doi:10.1117/12.2500209

27 April 2018 Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction

Theresa Scarnati, Anne Gelb

Proceedings Volume 10647, Algorithms for Synthetic Aperture Radar Imagery XXV; 106470R (2018) https://doi.org/10.1117/12.2500209
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

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.

Conference Presentation

Citation Download Citation

Theresa Scarnati and Anne Gelb "Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction", Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470R (27 April 2018); https://doi.org/10.1117/12.2500209

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