A sparse prior based statistical inversion approach for aperture synthesis radiometric imaging of extended source

被引：0

作者：

He, Fang-Min ^{[1
,2
]}

Li, Qing-Xia ^{[2
]}

Zhao, Zhi-Hua ^{[1
]}

Chen, Ke ^{[2
]}

机构：

[1] Research Institute of Power Technique, Navy University of Engineering, Wuhan

[2] Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2013年 / 41卷 / 03期

关键词：

Aperture synthesis; Extended source; Radiometer; Sparse prior; Statistical inversion;

D O I：

10.3969/j.issn.0372-2112.2013.03.001

中图分类号：

学科分类号：

摘要：

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.

引用

页码：417 / 423

页数：6

共 32 条

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