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Proceedings Paper

Genetic-algorithm-directed polarimetric sensing for optimum pattern classification
Author(s): Firooz A. Sadjadi
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Paper Abstract

In this paper an automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses probabilistic patterns separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions, the Rayleigh quotient, Bhattacharyya, Divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian Probability of Error, are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angle and the angle of polarization ellipse. The results indicate that based on the majority of the distance functions used; there is a unique set of state of polarization angles whose use will lead to improved classification performance.

Paper Details

Date Published: 22 October 2004
PDF: 11 pages
Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); doi: 10.1117/12.563889
Show Author Affiliations
Firooz A. Sadjadi, Lockheed Martin Corp. (United States)


Published in SPIE Proceedings Vol. 5557:
Optical Information Systems II
Bahram Javidi; Demetri Psaltis, Editor(s)

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