The multitemporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this article. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

中文翻译：

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分布式散射体相历史检索的自适应相位优化算法


基于分布式散射体（DSs）的多时相干涉合成孔径雷达（InSAR）技术在高精度变形测量中得到广泛应用，弥补了持续散射体InSAR技术无法获得足够监测点的缺陷，特别是在农村地区。考虑到DS像素容易受到各种去相关因素的影响，有必要通过相位优化算法（POA）来检索最佳相位序列。然而，传统的 POAs 依赖于通过空间平均统计均匀像素邻域导出的样本协方差矩阵或复相干矩阵 (CCM)，这可能会模糊和破坏相位信息，尤其是在密集的边缘区域。为了克服这个限制，本文提出了自适应 POA。自适应POA通过主相位分量估计和快速非局部均值滤波的空间自适应滤波方法融合得到的滤波干涉相位以及通过相干估计偏差校正确定的精确相干矩阵人为地构建了优越的CCM。此外，提出了一种改进的基于特征分解的相干功率加权干涉相位（EMI）最大似然估计器，以进一步提高估计精度和计算效率。然后使用改进的相干功率加权 EMI 算法处理估计的 CCM，并检索最佳相位历史。针对模拟数据和 Sentinel-1A 数据进行验证的实验结果表明，自适应 POA 比传统 POA 具有卓越的优化性能和鲁棒性。