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Proceedings Paper

Segmentation-directed SAR image compression via hierarchical stochastic modeling
Author(s): Andrew J. Kim; A. Hamid Krim; Alan S. Willsky
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Paper Abstract

There has recently been a growing interest in SAR imaging on account of its importance in a variety of applications. One reason for its gain in popularity is its ability to image terrain at extraordinary rates. Acquiring data at such rates, however, has drawbacks in the from of exorbitant costs in data storage and transmission over relatively slow channels. To alleviate these and related costs, we propose a segmentation driven compression technique using hierarchical stochastic modeling within a multiscale framework. Our approach to SAR image compression is unique in that we exploit the multiscale stochastic structure inherent in SAR imagery. This structure is well captured by a set of scale auto-regressive models that accurately characterize the evolution in scale of homogeneous regions for different classes of terrain. We thus use them to generate a multiresolution segmentation of the image. We subsequently use the segmentation in tandem with the corresponding models within a pyramid encoder to provide a robust, hierarchical compression technique which in addition to coding the segmentation, codes the image with high compression ratios and remarkable image quality.

Paper Details

Date Published: 3 April 1997
PDF: 12 pages
Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); doi: 10.1117/12.271730
Show Author Affiliations
Andrew J. Kim, Massachusetts Institute of Technology (United States)
A. Hamid Krim, Massachusetts Institute of Technology (United States)
Alan S. Willsky, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3078:
Wavelet Applications IV
Harold H. Szu, Editor(s)

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