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Proceedings Paper

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

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.

Paper Details

Date Published: 24 August 2000
PDF: 11 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396322
Show Author Affiliations
Vidya Venkatachalam, Rice Univ. (United States)
Hyeokho Choi, Rice Univ. (United States)
Richard G. Baraniuk, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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