Low scattering terrain areas introduce complex phase interference, which reduces the accuracy of deformation signal estimation in InSAR(Interferometric Synthetic Aperture Radar) techniques. Existing covariance matrix-based InSAR phase calculation methods often fail to account for translational offset relations between scatterers leading to inaccuracies, and pixels with zero spatial coherence exist. To address this issue, this paper proposes a distributed scatterer InSAR phase estimation method based on the Cross-Correlation complex coherence matrix. The effectiveness and superiority of the algorithm are verified through simulation and actual data. The results show that: (i) The simulation analysis shows that, compared to the traditional covariance matrix method, the optimal Cross-Correlation matrix improves the interferometric phase, coherence, and accuracy by 21.51%, 15.24%, and 6.52%, respectively. (ii) The actual experimental data show that the interferometric phase optimal by the Cross-Correlation matrix can effectively overcome the pseudo-signal caused by spatial hopping and make the phase more continuous. Compared with the traditional covariance matrix, the average a posteriori coherence and average coherence of arbitrary interference combinations in the Cross-Correlation matrix are improved by 18.12% and 58.10%, respectively. (iii) The number of DS points selected by the Cross-Correlation matrix algorithm is more than that of the covariance matrix algorithm. PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) achieved more accurate deformation rates compared to the covariance and correlation matrices, with errors of 9.34, 17.21, and 16.28 mm∙a-1 when compared against GNSS data, respectively. (iv) The Cross-Correlation matrix reduces the deformation rate error by 5.43 % relative to the covariance matrix. The algorithm provides reliable phase estimation for accurate monitoring of surface deformation in low-scattering regions, supporting geological disaster early warning and resource and environmental management.

中文翻译：

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基于互相关复相干矩阵的分布式散射体InSAR相位估计最优算法


低散射地形区域引入了复杂的相位干扰，降低了InSAR（Interferometric Synthetic Aperture Radar，干涉合成孔径雷达）技术中形变信号估计的准确性。现有的基于协方差矩阵的 InSAR 相位计算方法往往无法考虑散射体之间的平移偏移关系，导致不准确，并且存在空间相干性为零的像素。针对这一问题，该文提出了一种基于互相关复相干矩阵的分布式散射体InSAR相位估计方法。通过仿真和实际数据验证了该算法的有效性和优越性。结果表明：（i） 仿真分析表明，与传统的协方差矩阵方法相比，最优互相关矩阵的干涉相位、相干性和精度分别提高了 21.51%、15.24% 和 6.52%。（ii） 实际实验数据表明，通过互相关矩阵优化的干涉相位可以有效克服空间跳跃引起的伪信号，使相位更加连续。与传统协方差矩阵相比，互相关矩阵中任意干涉组合的平均后验相干性和平均相干性分别提高了 18.12% 和 58.10%。（iii） 互相关矩阵算法选择的 DS 点数多于协方差矩阵算法。与协方差和相关矩阵相比，PS-InSAR （Persistent Scatterer Interferometric Synthetic Aperture Radar） 实现了更准确的形变率，与 GNSS 数据相比，误差分别为 9.34、17.21 和 16.28 mm∙a-1。 （iv） 相对于协方差矩阵，互相关矩阵将变形率误差降低了 5.43 %。该算法为准确监测低散射区域的表面变形提供了可靠的相位估计，支持地质灾害预警和资源环境管理。