DS-InSAR phase optimization based on singular value decomposition

PENG Kai; ZHAO Feng; WANG Yunjia; YAN Shiyong; FENG Han

doi:10.11834/jrs.20210454

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DS-InSAR phase optimization based on singular value decomposition
Vol. 27, Issue 2, Pages: 533-542(2023)
Published： 07 February 2023 ，
DOI： 10.11834/jrs.20210454

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彭锴，赵峰，汪云甲，闫世勇，冯瀚.2023.基于奇异值分解的DS-InSAR相位优化.遥感学报，27（2）： 533-542

Peng K，Zhao F，Wang Y J，Yan S Y and Feng H. 2023. DS-InSAR phase optimization based on singular value decomposition. National Remote Sensing Bulletin， 27（2）：533-542
彭锴，赵峰，汪云甲，闫世勇，冯瀚.2023.基于奇异值分解的DS-InSAR相位优化.遥感学报，27（2）： 533-542 DOI： 10.11834/jrs.20210454.

Peng K，Zhao F，Wang Y J，Yan S Y and Feng H. 2023. DS-InSAR phase optimization based on singular value decomposition. National Remote Sensing Bulletin， 27（2）：533-542 DOI： 10.11834/jrs.20210454.

摘要

高质量监测点空间密度是时序InSAR开展形变监测的重要指标，依靠分布式散射体DS（Distributed Scatters）开展InSAR形变监测可有效解决传统时序InSAR监测点空间密度不够的缺陷，但分布式散射体干涉相位极易受到去相干影响，造成干涉相位失真，因此分布式散射体相位优化是DS-InSAR技术的关键，针对这一情况，本文提出一种新的基于奇异值分解的DS相位优化方法，该方法利用同质像元时序相位重构相位矩阵，对矩阵进行主成分分析得到优化相位。采用模拟数据和33景覆盖郑州东部白沙镇Sentinel-1A数据对提出方法的可靠性与有效性进行验证和分析。采用时序平均相位标准偏差、平均相位梯度、平均残差点数目作为干涉图优化效果评价指标，提出方法优化后干涉图相较于原始干涉图分别下降了15.61%、25.81%、44.84%，与对比DS相位优化方法相比，提出方法对干涉图DS相位优化效果更好，特别是在一些低相干区域仍然可获得较好DS相位优化结果；提出方法在降低DS相位噪声同时可较好地保持地物细节信息。此外，相较于常规PS（Permanent Scatters）技术形变监测结果，本文方法高质量监测点数量由121471个提升至644789个，提高了4.3倍，较对比方法高质量监测点密度提升更显著。模拟与真实数据结果证实了本文提出DS优化方法的有效性，该方法可用于基于DS-InSAR技术的地表形变监测。

Abstract

The spatial density of high-quality monitoring points is an important indicator for time-series InSAR to carry out deformation monitoring. Relying on Distributed Scatterers (DS) to carry out InSAR deformation monitoring can effectively solve the defect of insufficient spatial density of traditional time-series InSAR monitoring points

but the interferometric phase of distributed scatterers is easily affected by decoherence

causing the interferometric phase distortion and unreliable

so the phase optimization of distributed scatterers is the key to DS-InSAR technology and is particularly significant. Aiming at this situation

this paper proposes a new DS phase optimization method based on singular value decomposition. This method reconstructs the phase matrix by using the time-series phase of homogeneous pixels

which belong to the same substance within the inspection window

and performs principal component analysis on the matrix to obtain the optimized phase. As a very necessary step

simulation data and 33 scene coverages of Sentinel-1A data in Baisha town

eastern Zhengzhou

are used to verify the reliability and validity of the proposed method. Using time-series average phase standard deviation

average phase gradient

and average number of residual points as the evaluation index of interferogram optimization effect

the index of the interferogram optimized by the proposed method is reduced by 15.61%

25.81%

and 44.84% respectively compared with the original interferogram

which shows that these indicators have significant decreases. The results show that

compared with the contrast DS phase optimization method

the proposed method has a better effect on the interferogram DS phase optimization

especially in some areas with poor coherence and low signal to noise ratio. In addition

the proposed method can better maintain the detailed information of the ground features while reducing the DS phase noise. Besides

compared with the deformation monitoring results of the conventional PS(Permanent Scatterers)-InSAR technology

the number of high-quality monitoring points in this method has increased from 121471 to 644789

an increase of 4.3 times

and the density of high-quality monitoring points has increased more significantly than the comparison method. The experimental results of the simulation and real data confirm the effectiveness of the DS optimization method proposed in this paper

which can be used in DS-InSAR technology for surface deformation monitoring.

关键词

遥感DS-InSAR分布式散射体相位优化奇异值分解形变监测

Keywords

remote sensingDS-InSARdistributed scatterersphase optimizationsingular value decompositiondeformation monitoring

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