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Mathematical Problems in Engineering
Volume 2015, Article ID 864019, 11 pages
Research Article

Synthetic Aperture Radar Image Background Clutter Fitting Using SKS + MoM-Based Distribution

1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
2College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology, Mianyang, Sichuan 621010, China

Received 10 April 2015; Revised 18 August 2015; Accepted 6 September 2015

Academic Editor: Dan Simon

Copyright © 2015 Zhengwei Zhu et al. 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.


distribution can accurately model various background clutters in the single-look and multilook synthetic aperture radar (SAR) images and is one of the most important statistic models in the field of SAR image clutter modeling. However, the parameter estimation of distribution is difficult, which greatly limits the application of the distribution. In order to solve the problem, a fast and accurate distribution parameter estimation method, which combines second-kind statistics (SKS) technique with Freitas’ method of moment (MoM), is proposed. First we deduce the first and second second-kind characteristic functions of distribution based on Mellin transform, and then the logarithm moments and the logarithm cumulants corresponding to the above-mentioned characteristic functions are derived; finally combined with Freitas’ method of moment, a simple iterative equation which is used for estimating the distribution parameters is obtained. Experimental results show that the proposed method has fast estimation speed and high fitting precision for various measured SAR image clutters with different resolutions and different number of looks.