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Multiscale SAR Image Segmentation using Wavelet-domain Hidden Markov Tree Models

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Title: Multiscale SAR Image Segmentation using Wavelet-domain Hidden Markov Tree Models
Author: Venkatachalam, Vidya; Choi, Hyeokho; Baraniuk, Richard G.
Type: Conference paper
Keywords: segmentation; wavelet; SAR; ATR; MSTAR
Citation: V. Venkatachalam, H. Choi and R. G. Baraniuk, "Multiscale SAR Image Segmentation using Wavelet-domain Hidden Markov Tree Models," vol. 4053, 2000.
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.
Date Published: 2000-04-01

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  • ECE Publications [1048 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.