TY - GEN
T1 - Variance based joint sparsity reconstruction of synthetic aperture radar data for speckle reduction
AU - Scarnati, Theresa
AU - Gelb, Anne
N1 - Funding Information:
Anne Gelb’s work is supported in part by the grants NSF-DMS 1502640, NSF-DMS 1732434, and AFOSR FA9550-15-1-0152. Theresa Scarnati’s work is supported in part by AFRL and an AFOSR Lab Task. Approved for public release. PA Approval #:[88AWB-2018-2146].
Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.1117/12.2500209
DO - 10.1117/12.2500209
M3 - Conference contribution
AN - SCOPUS:85048433473
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Algorithms for Synthetic Aperture Radar Imagery XXV
A2 - Zelnio, Edmund
A2 - Garber, Frederick D.
PB - SPIE
T2 - Algorithms for Synthetic Aperture Radar Imagery XXV 2018
Y2 - 19 April 2018 through 19 April 2018
ER -