计算机应用

• 图形图像与信号处理 • 上一篇    下一篇

基于遗传算法的SAR图像多尺度分割

刘保利   

  1. 西北工业大学计算机科学技术学院;空军工程大学理学院电子系
  • 收稿日期:2007-08-10 修回日期:1900-01-01 发布日期:2008-04-01 出版日期:2008-04-01
  • 通讯作者: 刘保利

Genetic algorithm-based multiscale segmentation of SAR image

Liu Bao-li   

  • Received:2007-08-10 Revised:1900-01-01 Online:2008-04-01 Published:2008-04-01
  • Contact: Liu Bao-li

摘要: 基于最大期望(EM)算法与遗传算法(GA),提出一种有效的多尺度SAR图像无监督分割方法。该方法首先利用混合多尺度自回归(MMAR)模型描述SAR图像中由于雷达斑点所引起的不同尺度和同一尺度内像素之间的统计相依性; 然后将GA与EM结合给出MMAR模型的参数估计算法。这种算法利用最小描述长度(MDL)准则,能够选择模型的分量数;最后利用Bayes分类器实现图像的分割。该方法集遗传算法和EM算法的优点,对初始值有较少的敏感性,避免局部最优解,提高了分割精度。实验结果表明GAEM方法优于EM算法。

关键词: 最大期望算法, 遗传算法, 混合多尺度模型, SAR图像分割

Abstract: An effective unsupervised multiscale segmentation of Synthetic Aperture Radar(SAR) imagery based on Expectation Maximization (EM) and Genetic Algorithm(GA) was proposed. The statistical variations between pixels of scale-to-scale and same scale in SAR imagery were described due to radar speckle for the Mixture Multiscale AutoRegressive(MMAR) model, then the estimation of parameters in MMAR model was given by combining GA with EM algorithm. The number of components of the model was selected by using the Minimum Description Length (MDL) criterion and the segmentation of SAR imagery was implemented. This approach benefits from the properties of GA and the EM algorithm by combining of both into a single procedure. The local optimal solutions were avoided with less sensitivity to its initialization. The experiments on SAR images show that the GA-EM outperforms the EM method.

Key words: expectation maximization algorithm, Genetic Algorithm (GA), mixture mutiscale autoregressive model, segmentation of SAR imagery