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Proceedings Paper

Polarmetric classification of scattering centers
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Paper Abstract

Polarimetric diversity can be exploited in synthetic aperture radar (SAR) for enhanced target detection and target description. Detection statistics and target features can be computed from either polarimetric imagery or parametric processing of SAR phase histories. We adopt an M- ary Bayes classification approach and derive Bayes-optimal decision rules for detection and description of scattering centers. Scattering centers are modeled as one of M canonical geometric types with unknown amplitude, phase and orientation angle; clutter is modeled as one of M canonical geometric types with unknown amplitude, phase and orientation angel; clutter is modeled as a spherically invariant random vector. For the Bayes optimal decision rules, we provide a simple geometric interpretation and an efficient computational implementation. Moreover, we characterize the certainty of decisions by deriving an approximate posteriori probability.

Paper Details

Date Published: 10 June 1996
PDF: 11 pages
Proc. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, (10 June 1996); doi: 10.1117/12.242049
Show Author Affiliations
Emre Ertin, The Ohio State Univ. (United States)
Lee C. Potter, The Ohio State Univ. (United States)


Published in SPIE Proceedings Vol. 2757:
Algorithms for Synthetic Aperture Radar Imagery III
Edmund G. Zelnio; Robert J. Douglass, Editor(s)

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