Multiscale SAR image segmentation using wavelet-domain hidden Markov tree models

Vidya Venkatachalam, Hyeokho Choi, Richard G. Baraniuk

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

17 Scopus citations


We study the segmentation of SAR imagery using wavelet-domain Hidden Markov Tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of the wavelet transforms of images. This technique has been successfully applied to the segmentation of natural texture images, documents, etc. However, SAR image segmentation poses a difficult challenge owing to the high levels of speckle noise present at fine scales. We solve this problem using a 'truncated' wavelet HMT model specially adapted to SAR images. This variation is built using only the coarse scale wavelet coefficients. When applied to SAR images, this technique provides a reliable initial segmentation. We then refine the classification using a multiscale fusion technique, which combines the classification information across scales from the initial segmentation to correct for misclassifications. We provide a fast algorithm, and demonstrate its performance on MSTAR clutter data.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSociety of Photo-Optical Instrumentation Engineers
Number of pages11
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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