基于狼群算法的SAR图像属性散射中心参数估计

Parameter estimation of attributed scattering centers in SAR images based on wolf pack algorithm

  • 摘要: 针对合成孔径雷达(SAR)图像属性散射中心估计问题,提出基于狼群算法的新思路。方法首先在图像域上对SAR图像进行“分治”解耦。对每一个属性散射中心进行序贯估计时,采用狼群算法作为基础优化算法,获得散射中心最佳的参数集。狼群算法通过分析狼群的协作捕猎活动及猎物分配等特点,具备良好的全局搜索能力和局部开发能力。算法通过结合传统图像域解耦的思想和狼群算法的稳健优化性能,提高SAR图像整体的属性散射中心估计精度。实验中,采用所提方法对MSTAR数据集中的原始SAR图像及加噪样本进行参数估计,实验结果验证了其有效性和噪声稳健性。

     

    Abstract: For the attributed scattering center estimation problem of synthetic aperture radar (SAR) images, a new idea based on wolf pack algorithm was proposed. The method first decoupled the SAR image using the “divide and conquer” strategy. Afterwards, the wolf pack algorithm was adopted as the basic optimization method to sequentially estimate individual scattering centers with the optimal parameter sets. By analyzing the characteristics of cooperative hunting activities and prey distribution, the wolf pack algorithm has good global search ability and local development ability. The algorithm combines the traditional image decoupling with the robust estimation capability of wolf pack algorithm. Hence, the estimation precision of the overall SAR image can be improved. In the experiments, the proposed method was tested on the original SAR images and noisy samples based on the MSTAR dataset. The results validate the effectiveness and noise-robustness of the proposed method.

     

/

返回文章
返回