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Optical Engineering

Synthetic aperture radar surface reflectivity estimation using a marked point-process speckle model
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Paper Abstract

This paper presents stochastic models and estimation algorithms for speckled images, with an emphasis on synthetic-aperture-radar images, and where the speckle may not be fully developed. We treat speckle from a novel point of view: as a carrier of useful surface information rather than as contaminating noise. The stochastic models for surface scattering are based on a doubly stochastic marked Poisson point process. For each of these surface-scattering statistical models, we present estimation algorithms to determine the average surface reflectivity and scatterer density within a resolution cell, using intensity measurements of speckled images. We show that the maximum-likelihood estimator is optimal in the sense that the variance of the error is the smallest possible using any conceivable estimate having the same bias with the same data.

Paper Details

Date Published: 1 January 2003
PDF: 17 pages
Opt. Eng. 42(1) doi: 10.1117/1.1523052
Published in: Optical Engineering Volume 42, Issue 1
Show Author Affiliations
Jihad Salah Daba, Purdue Univ. (United States)
Mark R. Bell, Purdue Univ. (United States)

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