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

Theresa Scarnati, Anne Gelb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAlgorithms for Synthetic Aperture Radar Imagery XXV
EditorsEdmund Zelnio, Frederick D. Garber
PublisherSPIE
ISBN (Electronic)9781510618053
DOIs
StatePublished - 2018
Externally publishedYes
EventAlgorithms for Synthetic Aperture Radar Imagery XXV 2018 - Orlando, United States
Duration: Apr 19 2018Apr 19 2018

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10647
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherAlgorithms for Synthetic Aperture Radar Imagery XXV 2018
CountryUnited States
CityOrlando
Period4/19/184/19/18

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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