基于物理启发机器学习的属性散射中心提取方法

Extraction of Attributed Scattering Center Based on Physics Informed Machine Learning

基于参数化散射中心模型进行参数估计是实现合成孔径雷达高级信息获取(SAR AIR)技术的基本思路之一,传统的属性散射中心(ASC)参数估计算法往往具有计算速度慢、算法复杂度高、对参数初值要求高等问题。对此,该文提出一个新的基于无监督学习的端到端框架用于从SAR图像反演ASC参数。首先,利用自编码式网络结构有效提取目标图像特征,缓解由于优化空间复杂非凸导致的直接求解困难,解决初值敏感问题;其次,通过嵌入ASC模型作为物理解码器以将编码器输出约束为正确的ASC参数;最后,通过端到端的模型架构进行学习和推理,达到降低算法复杂度及提高估计速度的目的。通过在仿真和实测数据上进行测试,实验结果表明在0.15 m分辨率测试集SAR图像上取得低于0.1 m的估计误差,反演单个散射中心平均耗时0.06 s,验证了该文所提方法的有效性、高效性与鲁棒性。

To estimate parameters of parameterized scattering center models is one of the basic methods for Synthetic Aperture Radar Advanced Information Retrieval(SAR AIR).Traditional Attributed Scattering Center(ASC)parameter estimation algorithms usually suffer from issues such as slow computation speed,high algorithm complexity,and high sensitivity to initial values of parameters.In this paper,a novel end-to-end framework for inverting ASC parameters from radar images based on unsupervised deep learning is proposed.Firstly,an autoencoder network structure is employed to effectively extract image features of targets,alleviating the difficulties solving directly caused by the complex non-convex optimization space and resolving the sensitivity to initial values.Secondly,the ASC model is embedded as a physical decoder to constrain the encoder output to correct ASC parameters.Finally,the end-to-end architecture are utlized to train and infer the model,achieving the purpose of reducing algorithm complexity and improving estimation speed.Through testing on simulated and measured data,experimental results indicate that the estimation error obtained on the SAR image test set with a resolution of 0.15 m is less than 0.1 m while the average processing time is 0.06 s for the inversion of one single scattering center,which demonstrate the effectiveness,efficiency,and robustness of the proposed approach.