Unsupervised SAR image segmentation using recursive partitioning

Vidya Venkatachalam, Robert D. Nowak, Richard G. Baraniuk, Mario A T Figueiredo

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

5 Scopus citations


We present a new approach to SAR image segmentation based on a Poisson approximation to the SAR amplitude image. It has been established that SAR amplitude images are well approximated using Rayleigh distributions. We show that, with suitable modifications, we can model piecewise homogeneous regions (such as tanks, roads, scrub, etc.) within the SAR amplitude image using a Poisson model that bears a known relation to the underlying Rayleigh distribution. We use the Poisson model to generate an efficient tree-based segmentation algorithm guided but the minimum description length (MDL) criteria. We present a simple fixed tree approach, and a more flexible adaptive recursive partitioning scheme. The segmentation is unsupervised, requiring no prior training, and very simple, efficient, and effective for identifying possible regions of interest (targets). We present simulation results on MSTAR clutter data to demonstrate the performance obtained with this parsing technique.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages9
StatePublished - 2000
EventAlgorithms for Synthetic Aperture Radar Imagery VII - Orlando, FL, USA
Duration: Apr 24 2000Apr 28 2000


OtherAlgorithms for Synthetic Aperture Radar Imagery VII
CityOrlando, FL, USA

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics


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