附加系统参数的多时相InSAR时空建模和形变估计
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P237

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国家自然科学基金(41474007,41404013,41222027)


Spatio-temporal modeling and deformation estimation of multi-temporal InSAR with additional systematical parameters
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    摘要:

    轨道误差和长波大气延迟组成的系统误差是影响InSAR形变监测精度的重要因素之一.传统方法在空间域对干涉图的系统误差建模,容易导致长波形变和系统误差相混淆.本文在时空域利用附加系统参数对系统误差建模,同时根据观测值质量对差分相位观测值定权,采用附加系统参数的加权最小二乘法估计形变参数和系统误差,实现了长波形变和系统误差的分离.模拟实验结果表明,在形变与系统误差的空间变化特性完全一致的极端情况下,本文方法能实现两者的有效分离,估计的形变速率均方根误差比传统方法降低了98.8%.ASAR数据实验显示当形变尺度较小且分散分布时,本文方法和传统方法得到的结果相似;当形变在研究区内表现为长波变化时,本文方法比传统方法估计的形变结果更为稳健.

    Abstract:

    The systematic error of InSAR is composed of orbit error and long-wavelength atmospheric delay,which is one of the most important factors that affect the accuracy of InSAR deformation monitoring.Traditional method models the systematic error of each individual interferogram in spatial domain,so it is difficult to distinguish between the long-wavelength deformation and systematic error.In this study,we modeled the systematic errors in spatio-temporal domain,determined the weight matrix of the observations according to its quality,and estimated the deformation parameters and systematic error by the weighted least square method.The simulation results show that,even in the extreme cases where the spatial characteristics of deformation and systematic error are completely consistent,the proposed method can separate the deformation and systematic errors effectively.The deformation rate RMSE of the proposed method is 98.8% lower than that of traditional method.The experiment with ASAR shows that,the proposed method and traditional method obtain similar results when the deformation scale is small and scattered over the study area.However,when large scale long-wavelength deformation occurs in the study area,the results obtained by the proposed method are more robust than those obtained by traditional method.

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韦建超,李志伟,段梦,曹云梦,冯光财,胡俊.附加系统参数的多时相InSAR时空建模和形变估计[J].南京信息工程大学学报(自然科学版),2020,12(2):223-230
WEI Jianchao, LI Zhiwei, DUAN Meng, CAO Yunmeng, FENG Guangcai, HU Jun. Spatio-temporal modeling and deformation estimation of multi-temporal InSAR with additional systematical parameters[J]. Journal of Nanjing University of Information Science & Technology, 2020,12(2):223-230

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  • 收稿日期:2019-10-15
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  • 在线发布日期: 2020-04-08
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