Statistical modeling and analysisof high-resolution Synthetic Aperture Radar images

  • Authors:

  • Shyam Kuttikkad;Rama Chellappa

  • Affiliations:

  • Etak, Inc., 1605 Adams Drive, Menlo Park, California 94025, USA. shyam.kuttikad@etak.com;Department of Electrical Engineering and Center for Automation Research, 2365 AV Williams Blg., University of Maryland, College Park, Maryland 20742, USA. chella@eng.umd.edu

  • Venue:
  • Statistics and Computing
  • Year:
  • 2000

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Abstract

A Synthetic Aperture Radar (SAR) is an imagingsensor capable of capturing high-resolution aerial images under avariety of imaging conditions. SAR images find application in remotesensing and military target detection and surveillance. Since SARimages exhibit considerable variations in signal strength, even whenimaging similar features or objects belonging to the same class,probabilistic descriptions are useful for modeling SAR data. Thispaper includes an overview of popular statistical distributions usedto model real, complex, and polarimetric SAR images. Specializedtechniques are necessary for analyzing SAR images due to their uniquecharacteristics when compared to aerial images produced by othersensors. We focus on two distinct methods of SAR image analysis inthis paper: Constant false alarm rate processing for targetdetection; and pixel classification using statistical models.Previous work done in each of these areas is reviewed and compared.Some of the popular image analysis techniques are illustrated withexperimental results from real high-resolution SAR data.