电子学报 ›› 2013, Vol. 41 ›› Issue (1): 178-184.DOI: 10.3969/j.issn.0372-2112.2013.01.031

• 科研通信 • 上一篇    下一篇

基于EM算法的G0分布参数最大似然估计

周鑫   

  1. 南京航空航天大学自动化学院,江苏南京 210016
  • 收稿日期:2012-05-14 修回日期:2012-08-30 出版日期:2013-01-25
    • 作者简介:
    • 周 鑫 男,1980年1月出生,江苏丹阳人.南京航空航天大学自动化学院副教授.2008年在弗吉尼亚大学获博士学位.主要研究方向为目标检测和识别,图像处理和分析. E-mail:xzhou@nuaa.edu.cn
    • 基金资助:
    • 国家自然科学基金 (No.61102138); 南京航空航天大学基本科研业务费专项基金 (No.201090)

An EM Algorithm Based Maximum Likelihood Parameter Estimation Method for the G0 Distribution

ZHOU Xin   

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China
  • Received:2012-05-14 Revised:2012-08-30 Online:2013-01-25 Published:2013-01-25
    • Supported by:
    • National Natural Science Foundation of China (No.61102138); Fundamental Research Funds for Nanjing University of Aeronautics and Astronautics (No.201090)

摘要: G0分布是目前合成孔径雷达(Synthetic Aperture Radar,SAR)图像数据建模的一个重要模型,建模能力强、实用性好,受到了广泛的关注.G0分布的应用离不开准确有效的参数估计,而由于G0分布表达式复杂,统计意义上最优的最大似然估计法一直没能用在G0分布上.本文首先给出了一种新的方式来推导得出G0分布,在此基础上,采用最大期望(Expectation Maximization,EM)算法为G0分布给出一种有效的最大似然参数估计方法.文中的方法与现有的G0分布参数估计方法通过实验进行了比较,实验结果充分证明了所提方法的有效性.

关键词: SAR图像, G0分布, EM算法, 最大似然估计

Abstract: As an important model for modeling synthetic aperture radar (SAR) image data,G0 distribution has strong modeling ability and good practicability,and therefore draws extensive attentions around the world.The applications of G0 distribution require accurate and effective parameter estimations,and maximum likelihood estimator,which is statistically optimal,has not been applied for the G0 distribution due to the complexity of G0 distribution.In this paper,a new derivation of G0 distribution is first given,based on which,a maximum likelihood parameter estimation method using expectation maximization (EM) algorithm is proposed for the G0 ditribution.The proposed method is compared with other G0 parameter estimation methods through extensive experiments,and the results show the effectiveness of the proposed method.

Key words: SAR image, G0 distribution, EM algorithm, maximum likelihood estimation

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