Synthetic-aperture radar imagery scene segmentation using fractal      processing

Clayton V. Stewart; Baback Moghaddam; Kenneth J. Hintz

doi:10.1117/12.138236

9 July 1992 Synthetic aperture radar imagery scene segmentation using fractal processing

Clayton V. Stewart, Baback Moghaddam, Kenneth J. Hintz

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138236
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Clayton V. Stewart, Baback Moghaddam, and Kenneth J. Hintz "Synthetic aperture radar imagery scene segmentation using fractal processing", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138236

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