Paper
24 August 2000 Multiscale SAR image segmentation using wavelet-domain hidden Markov tree models
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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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vidya Venkatachalam, Hyeokho Choi, and Richard G. Baraniuk "Multiscale SAR image segmentation using wavelet-domain hidden Markov tree models", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396322
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CITATIONS
Cited by 21 scholarly publications.
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KEYWORDS
Image segmentation

Wavelets

Synthetic aperture radar

Wavelet transforms

Image processing

Data modeling

Image fusion

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