Genetic-algorithm-directed polarimetric sensing for optimum pattern classification

Firooz A. Sadjadi

doi:10.1117/12.563889

22 October 2004 Genetic-algorithm-directed polarimetric sensing for optimum pattern classification

Firooz A. Sadjadi

Proceedings Volume 5557, Optical Information Systems II; (2004) https://doi.org/10.1117/12.563889
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

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.

Citation Download Citation

Firooz A. Sadjadi "Genetic-algorithm-directed polarimetric sensing for optimum pattern classification", Proc. SPIE 5557, Optical Information Systems II, (22 October 2004); https://doi.org/10.1117/12.563889

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KEYWORDS

Genetic algorithms

Polarization

Polarimetry

Radar

Dielectric polarization

Optimization (mathematics)

Image classification

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