徐刚2,,
①.陕西黄河集团有限公司 ??西安 ??710043
②.东南大学信息科学与工程学院毫米波国家重点实验室 ??南京 ??210096
基金项目:国家自然科学基金项目(61701106)；江苏省自然科学基金项目(BK20170698)；陕西省创新人才推进计划-青年科技新星项目(S2019-ZC-XXXM-0035)
详细信息
作者简介:侯育星(1987–)，男，陕西西安人。陕西黄河集团有限公司设计研究所高级工程师，主要研究方向为雷达系统设计、雷达信号处理等。E-mail: houyuxing205@163.com
徐刚：徐 刚(1987–)，男，山东枣庄人。东南大学副教授，硕士生导师，主要研究方向为雷达信号处理、雷达高分辨成像以及毫米波雷达成像等。E-mail: gangxu@seu.edu.cn
通讯作者:徐刚 gangxu@seu.edu.cn
中图分类号:TN 957计量
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被引次数:0
出版历程
收稿日期:2018-11-26
修回日期:2018-12-18
网络出版日期:2019-01-23
Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach
Hou Yuxing1,,Xu Gang2,,
①. Shaanxi Huanghe Group Co., LTD, Xi’an 710043, China
②. State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China
Funds:The National Natural Science Foundation of China (61701106), The Natural Science Foundation of Jiangsu Province (BK20170698), The Innovative Talent Promotion Program of Shaanxi Province-Youth Science and Technology New Star Project (S2019-ZC-XXXM-0035)
摘要
摘要:针对干涉合成孔径雷达(InSAR)成像，该文提出了一种通道联合结构化稀疏的贝叶斯成像算法，可实现图像稀疏特征化增强，以提升干涉相位噪声滤波和相干斑抑制性能。基于贝叶斯准则，利用多层级统计模型建立稀疏成像模型，结构化稀疏表示InSAR图像。在稀疏成像求解中，利用最大期望(EM)算法进行图像重构和多层级统计参数估计。由于能够联合利用通道稀疏统计特性，所提算法能够有效提升InSAR幅度和相位噪声滤波性能。最后，通过实验分析进一步验证该文算法的有效性。
关键词:干涉合成孔径雷达/
通道联合稀疏/
贝叶斯/
干涉相位滤波/
相干斑抑制
Abstract:A novel sparse Bayesian learning approach with a joint sparsity model is proposed for Interferometric Synthetic Aperture Radar (InSAR) image formation to realize the feature enhancements of interferometric phase denoising and speckle reduction. Using Bayesian rules, sparse image formation is achieved using a hierarchical statistical model. In particular, structured sparsity with joint channels is imposed on the InSAR images. During sparse imaging, an Expectation-Maximization (EM) method is employed for image formation and hyper-parameter estimation. Using joint sparsity statistics, the performance of the noise reduction on the magnitude and phase of InSAR images can be improved. Finally, experimental analysis is performed using simulated and measured data to confirm the effectiveness of the proposed algorithm.
Key words:Interferometric Synthetic Aperture Radar (InSAR)/
Joint sparsity/
Bayesian/
Interferometric phase de-noising/
Speckle reduction
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