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Feature Enhancement of Interferometric Synthetic Aperture Radar Image Formation Using Sparse Bayesian Learning in Joint Sparsity Approach
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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。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.
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表 1 本文算法流程框图
Table 1. Algorithm flow chart in this paper
InSAR稀疏贝叶斯特征化成像算法 输入：预处理多通道数据 ${{{{s}}}_l}$和观测矩阵 ${{{T}}_l}$ WHILE循环(符合迭代条件时) (1) 稀疏成像 (a) 利用式(3)更新自适应表征字典 ${{{Φ}}_l}$中的 ${{{P}}_l}$部分； (b) 利用式(10)和式(11)更新 ${{{μ}}_l}$和 ${{{Σ}}_l}$。 (2) 参数估计 (a) 利用式(16)估计 ${\gamma _{mn}}$和 $r$，更新协方差矩阵 ${{{Σ}}_b}$； (b) 利用式(17)更新噪声参数 $\alpha $。 END 输出： 特征化重构图像 ${\hat {{b}}_l}{\rm{ = }}{{{μ}}_l}$. 
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