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

Jihad Salah Daba; Mark R. Bell

doi:10.1117/1.1523052

1 January 2003 Synthetic aperture radar surface reflectivity estimation using a marked point-process speckle model

Jihad Salah Daba, Mark R. Bell

Author Affiliations +

Optical Engineering, Vol. 42, Issue 1, (January 2003). https://doi.org/10.1117/1.1523052

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.

©(2003) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Jihad Salah Daba and Mark R. Bell "Synthetic aperture radar surface reflectivity estimation using a marked point-process speckle model," Optical Engineering 42(1), (1 January 2003). https://doi.org/10.1117/1.1523052

Published: 1 January 2003

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE