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DS-InSAR phase optimization based on singular value decomposition

  • role： First author第一作者
  • Affiliation：

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    The Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology,Xuzhou 221116, China

  • Email：1634612853@qq.com
  • Introduction：彭锴，研究方向为InSAR理论及沉降监测应用。E-mail： 1634612853@qq.com
PENG Kai12,  
  • Affiliation：

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    The Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology,Xuzhou 221116, China

ZHAO Feng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    The Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology,Xuzhou 221116, China

  • Email：wyj4139@163.com
  • Introduction：汪云甲，研究方向为国土环境及灾害监测、室内外无缝定位、资源环境评价及管理。E-mail： wyj4139@163.com
WANG Yunjia12*,  
  • Affiliation：

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    The Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology,Xuzhou 221116, China

YAN Shiyong12,  
  • Affiliation：

    School of Environment Science and Spatial Information, China University of Mining and Technology, Xuzhou 221116, China

    The Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology,Xuzhou 221116, China

FENG Han12

реферат

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.

ключеви́че слова́

remote sensing;DS-InSAR;distributed scatterers;phase optimization;singular value decomposition;deformation monitoring

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