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Proceedings Paper

Synthetic-aperture radar imagery scene segmentation using fractal processing
Author(s): Clayton V. Stewart; Baback Moghaddam; Kenneth J. Hintz
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Paper Abstract

This paper demonstrates the application of fractal random process models and their related scaling parameters as features in the analysis and segmentation of clutter in high-resolution polarimetric synthetic aperture radar (SAR) imagery. Specifically, the fractal dimension of natural clutter sources, such as grass and trees, is computed and used as a texture feature for a Bayesian classifier. The SAR shadows are segmented in a separate manner using the original backscatter power as a discriminant. The proposed segmentation process yields a three-class segmentation map for the scenes considered in this study (with three clutter types: shadows, trees and grass). The difficulty of computing texture metrics in high-speckle SAR imagery is also addressed. In particular, a two-step preprocessing approach consisting of polarimetric minimum speckle filtering followed by non-coherent spatial averaging is used. The relevance of the resulting segmentation maps to constant-false-alarm-rate (CFAR) target detection techniques is also discussed.

Paper Details

Date Published: 9 July 1992
PDF: 7 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138236
Show Author Affiliations
Clayton V. Stewart, George Mason Univ. (United States)
Baback Moghaddam, George Mason Univ. (United States)
Kenneth J. Hintz, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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