Sensors 2010, 10(1), 775-795; doi:10.3390/s100100775
Review

Statistical Modeling of SAR Images: A Survey

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Received: 23 November 2009; in revised form: 5 January 2010 / Accepted: 6 January 2010 / Published: 21 January 2010
(This article belongs to the Section Remote Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It aims to describe SAR images through statistical methods and reveal the characteristics of these images. Moreover, statistical modeling can provide a technical support for a comprehensive understanding of terrain scattering mechanism, which helps to develop algorithms for effective image interpretation and creditable image simulation. Numerous statistical models have been developed to describe SAR image data, and the purpose of this paper is to categorize and evaluate these models. We first summarize the development history and the current researching state of statistical modeling, then different SAR image models developed from the product model are mainly discussed in detail. Relevant issues are also discussed. Several promising directions for future research are concluded at last.
Keywords: synthetic aperture radar (SAR) images; statistical models; parameter estimation; probability density function (PDF); the product model
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MDPI and ACS Style

Gao, G. Statistical Modeling of SAR Images: A Survey. Sensors 2010, 10, 775-795.

AMA Style

Gao G. Statistical Modeling of SAR Images: A Survey. Sensors. 2010; 10(1):775-795.

Chicago/Turabian Style

Gao, Gui. 2010. "Statistical Modeling of SAR Images: A Survey." Sensors 10, no. 1: 775-795.

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