A Sparse Prior Based Statistical Inversion Approach for Aperture Synthesis Radiometric Imaging of Extended Source

HE Fang-min; LI Qing-xia; ZHAO Zhi-hua; CHEN Ke

doi:10.3969/j.issn.0372-2112.2013.03.001

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A Sparse Prior Based Statistical Inversion Approach for Aperture Synthesis Radiometric Imaging of Extended Source

更新时间：2025-07-16

- A Sparse Prior Based Statistical Inversion Approach for Aperture Synthesis Radiometric Imaging of Extended Source
- Acta Electronica Sinica Vol. 41, Issue 3, Pages: 417-423(2013)
- 作者机构：
  
  1. 海军工程大学电力电子技术研究所,湖北,武汉,430033
  2. 华中科技大学电子与信息工程系,湖北,武汉,430074
  3. 海军工程大学电力电子技术研究所,湖北,武汉,430033
  4. 华中科技大学电子与信息工程系,湖北,武汉,430074
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61201055)
- DOI：10.3969/j.issn.0372-2112.2013.03.001
  CLC： TP722.6
- Published：2013
- 稿件说明：
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HE Fang-min, LI Qing-xia, ZHAO Zhi-hua, et al. A Sparse Prior Based Statistical Inversion Approach for Aperture Synthesis Radiometric Imaging of Extended Source[J]. Acta Electronica Sinica, 2013, 41(3): 417-423.
DOI：

HE Fang-min, LI Qing-xia, ZHAO Zhi-hua, et al. A Sparse Prior Based Statistical Inversion Approach for Aperture Synthesis Radiometric Imaging of Extended Source[J]. Acta Electronica Sinica, 2013, 41(3): 417-423. DOI： 10.3969/j.issn.0372-2112.2013.03.001.

摘要

确定性的综合孔径辐射计反演方法没有考虑亮温分布先验信息的统计特性.针对亮温分布具有非连续特性的展源

本文提出了一种基于稀疏先验的综合孔径展源辐射成像统计反演方法.根据该方法

采用修正的差分算子提取亮温非连续分布展源中隐含的稀疏先验

建立稀疏先验概率分布的多层先验等效高斯模型

将图像反演等效为该模型超参数估计

并采用期望最大化算法估计该模型超参数.仿真和实验结果表明:与现有的综合孔径辐射计确定性反演方法相比

本文提出的反演方法不仅能有效提高反演图像的准确度

而且对综合孔径辐射计的各种误差鲁棒性更强.

Abstract

In the deterministic inversion approaches for aperture synthesis radiometers (ASRs) imaging

the statistical property about the prior of brightness temperature distribution (BTD) is not taken into account.Aimed at the extended sources with discontinuous BTD

a sparse prior based statistical inversion approach (SIA) for ASRs imaging is proposed.According to the SIA

a modified difference operator is used to extract the implicit sparse prior about the discontinuous BTD of the extended sources

and the equivalent hierarchical prior Gaussian model for the sparse prior probability distribution is constructed.Then the inversion of ASRs is recast as hyperparameter estimation

and the expectation maximization algorithm is proposed to estimate the hyperparameter.The simulations and experiments show that the sparse prior based SIA can improve the radiometric accuracy of the reconstructed image and is more robust to the impacts of the imperfections of the ASRs as compared to the deterministic inversion approaches.

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