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

Statistical Modeling of SAR Images: A Survey

National University of Defence Technology, Changsha 410073, China
Received: 23 November 2009 / Revised: 5 January 2010 / Accepted: 6 January 2010 / Published: 21 January 2010
View Full-Text   |   Download PDF [288 KB, uploaded 21 June 2014]   |  


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 synthetic aperture radar (SAR) images; statistical models; parameter estimation; probability density function (PDF); the product model
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

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