计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (3): 214-216.

• 工程与应用 • 上一篇    下一篇

小波支持向量机在SAR图像降噪中的应用

张绍明,陈 鹰,林 怡   

  1. 同济大学 遥感与空间信息技术研究中心,上海 200092
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-01-21 发布日期:2008-01-21
  • 通讯作者: 张绍明

Application of wavelet support vector machine on SAR image denoising

ZHANG Shao-ming,CHEN Ying,LIN Yi   

  1. Research Center of Remote Sensing and Spatial Technology,Tongji University,Shanghai 200092,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-01-21 Published:2008-01-21
  • Contact: ZHANG Shao-ming

摘要: 基于支持向量回归理论和小波支持向量核函数,提出了一种新的SAR滤波方法。首先对支持向量回归方法做了分析,通过对复杂信号进行逼近实验,验证了其应用于图像滤波的可行性和合理性。之后将SAR图像看成是一个二维连续信号,将对复杂信号具有更好逼近能力的小波支持向量核函数用于SAR图像滤波,小波核函数由Morlet小波构建。实验结果表明本文提出的方法能很好的降低SAR图像噪声,而且能比传统方法更好的保持边缘。

关键词: 合成孔径雷达, 支持向量机, 核函数, 小波分析, 函数逼近

Abstract: A new filtering method for Synthetic Aperture Radar image is presented based on support vector regression and wavelet kernel function.The feasibility of SAR image fitting based on support vector machine is proved based on the analysis for support vector regression and the experiment of signal filtering.Then the SAR image is regarded as 2-dimension continuous signal and filtered by support vector regression with wavelet kernel function.The wavelet kernel is constructed by Morlet mother wavelet function.The results of experiment show that the method proposed in this paper could reduce the noise effective and keep the edge better than traditional ones.

Key words: Synthetic Aperture Radar(SAR), support vector machine, kernel function, wavelet analysis, function approximation