Feature extraction of sar target in clutter based on peak region segmentation and regularized orthogonal matching pursuit

Xunchao Cong, R. Zhu, Yu-Lin Liu, Q. Wan
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引用次数: 7

Abstract

Feature extraction in clutter is a challenging problem in SAR target recognition because of the difficulty in distinguishing the target signature from the background. In this paper, a new feature extracting algorithm based on automated peak region segmentation (PRS) and regularized orthogonal matching pursuit (ROMP) techniques is presented and called PRS-ROMP. It combines the processes in both signal domain and image domain. First, the proposed method uses PRS and parametric model (PM) to obtain the positions and atoms of strong scattering centers of target. Then we acquire the positions and atoms of weak scattering centers by the sparse reconstruction algorithm and PM for residual region. By using all atoms of strong and weak scattering centers we get the final amplitude estimation by LS. Experimental results of electromagnetic calculations data in clutter validate the proposed target feature extraction method.
基于峰区分割和正则化正交匹配追踪的杂波sar目标特征提取
杂波条件下的特征提取是SAR目标识别中的一个难点,因为杂波条件下的目标特征难以从背景中识别出来。本文提出了一种基于自动峰域分割(PRS)和正则化正交匹配追踪(ROMP)技术的特征提取算法,称为PRS-ROMP。它结合了信号域和图像域的处理。该方法首先利用PRS和参数化模型(PM)获取目标强散射中心的位置和原子;然后利用稀疏重建算法和残差区域的PM获取弱散射中心的位置和原子。利用强散射中心和弱散射中心的所有原子,我们得到了用LS估计的最终振幅。杂波环境下电磁计算数据的实验结果验证了所提出的目标特征提取方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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