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Proceedings Paper

Parameter-estimation techniques of statistical models and their application to SAR images
Author(s): George A. Lampropoulos; Rita Hui; Anastasios Drosopoulos
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Paper Abstract

In this paper we present a method to obtain a maximum likelihood estimation of the parameters of the generalized gamma and K probability density functions. Explicit closed form expressions are derived between the model parameters and the experimental data. Due to their nonlinear nature global optimization techniques are proposed for solving the derived expressings with respect to clutter model parameters. Experimental results show in all attempted cases that the resulting expressions are convex functions of the parameters. In addition to the maximum likelihood solution we present two other solutions. One is based on moment and the other on histogram matching. The Cramer-Rao lower bound is also derived and used for performance comparisons.

Paper Details

Date Published: 24 October 1997
PDF: 13 pages
Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); doi: 10.1117/12.279495
Show Author Affiliations
George A. Lampropoulos, A.U.G. Signals Ltd. (Canada)
Rita Hui, A.U.G. Signals Ltd. (Canada)
Anastasios Drosopoulos, Defence Research Establishment Ottawa (Canada)

Published in SPIE Proceedings Vol. 3162:
Advanced Signal Processing: Algorithms, Architectures, and Implementations VII
Franklin T. Luk, Editor(s)

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