A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks
A Modification to Phase Estimation for Distributed Scatterers in InSAR Data Stacks
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DOI：
10.3390/rs15030613
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Changjun Zhao;Yunyun Dong;Wenhao Wu;B. Tian;Jianmin Zhou;Ping Zhang;Shuo Gao;Y. Yu;Lei Huang
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文献类型：
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作者：
Changjun Zhao;Yunyun Dong;Wenhao Wu;B. Tian;Jianmin Zhou;Ping Zhang;Shuo Gao;Y. Yu;Lei Huang
为了提高多时相干涉合成孔径雷达测点的空间密度和质量，需要对其进行分布式散射体处理。在DS干涉测量中，一个基本的步骤是相位估计，它从所有可用的干涉图中重建出一致的相位序列。受相干估计的次优性的影响，现有的相位估计算法的性能严重下降。以前的研究已经通过引入相干偏差校正技术来解决这个问题。然而，由于校正能力有限，相位估计的精度仍然不足。本文提出了一种改进的相位估计方法。具体地说，通过结合关于干涉相干性和观看次数的信息，实现了对相干性幅度矩阵的每个元素的显著偏差校正。将纠偏后的相干矩阵与先进的统计均匀像素选择和时间序列相位优化算法相结合，得到最优的相位序列。仿真数据集和Sentinel-1真实数据集验证了该方法相对于传统的相位估计算法的优越性。具体地说，该方案能够以相当高的精度校正相干偏差。与现有的偏差校正方法相比，相干幅度的平均偏差降低了29%以上，标准偏差降低了18%以上。与现有方法相比，该方法在重建的相位序列上获得了更高的精度，包括更平滑的干涉相位和更少的离群值。
To improve the spatial density and quality of measurement points in multitemporal interferometric synthetic aperture radar, distributed scatterers (DSs) should be processed. An essential procedure in DS interferometry is phase estimation, which reconstructs a consistent phase series from all available interferograms. Influenced by the well-known suboptimality of coherence estimation, the performance of the state-of-the-art phase estimation algorithms is severely degraded. Previous research has addressed this problem by introducing the coherence bias correction technique. However, the precision of phase estimation is still insufficient because of the limited correction capabilities. In this paper, a modified phase estimation approach is proposed. Particularly, by incorporating the information on both interferometric coherence and the number of looks, a significant bias correction to each element of the coherence magnitude matrix is achieved. The bias-corrected coherence matrix is combined with advanced statistically homogeneous pixel selection and time series phase optimization algorithms to obtain the optimal phase series. Both the simulated and Sentinel-1 real data sets are used to demonstrate the superiority of this proposed approach over the traditional phase estimation algorithms. Specifically, the coherence bias can be corrected with considerable accuracy by the proposed scheme. The mean bias of coherence magnitude is reduced by more than 29%, and the standard deviation is reduced by more than 18% over the existing bias correction method. The proposed approach achieves higher accuracy than the current methods over the reconstructed phase series, including smoother interferometric phases and fewer outliers.