InSAR时序监测及应用中的质量控制研究
发布时间：2019-04-30 08:20
【摘要】：近年来，随着新型高分辨率SAR卫星的相继发射以及时序InSAR技术的不断进步和发展，InSAR技术以其难以抵挡的迅猛优势被广泛的应用在地震、地面沉降、滑坡、泥石流等地质灾害的调查监测研究中，从而为地球物理以及大地测量学等研究领域提供了一种全新的动态研究途径，是一种极具潜力和优势的空间对地观测新技术。 然而，由于InSAR数据常会受到大气、DEM、轨道以及失相干噪声等多项误差的影响，且这些误差往往具有多源性、复杂性、交叉性以及各种的不确定性等，使得InSAR监测数据中的部分误差难以消除，严重的影响了InSAR形变监测结果的精度和可靠性，从而使其无法达到地表分辨单元毫米级微小形变的理论监测精度，严重的制约了InSAR技术的进一步推广和应用。因此如何对InSAR数据处理中的多种误差进行质量控制，即对InSAR数据中的异常数据、粗差、缺失或过多的冗余信息进行分析，是获取高精度和高可靠性的最优InSAR监测结果的一个迫切需求，，是进行InSAR监测结果后处理和形变机理反演和预警的重要前提。基于此，本文在对InSAR数据处理中的各种误差特性进行统计分析的基础上，重点针对InSAR监测数据中存在的各种误差问题，研究了其相应的误差消除方法；并从大地测量学理论出发，通过建立数学模型，运用合理的算法来消除InSAR数据中的误差项，从而保证了高精度高可靠性的InSAR监测数据获取。 通过研究，本文取得了以下主要创新性成果： 1）在研究InSAR相位解缠方法的基础上，针对InSAR数据中存在的解缠误差，提出了一种基于移动开窗多面函数法的InSAR解缠相位重构模型：多面函数法保证了解缠相位的连续性，移动开窗法则保持了相位的局部细节信息。并在构建模型时给出了顾及相干性约束和特征相位的InSAR解缠相位拟合节点确定方法；最后利用F统计对重构模型进行了显著性检验。 2）针对InSAR数据中存在的轨道误差，在线性拟合估计方法的基础上，提出了一种基于小波分解的抗差最小二乘方法对轨道残余干涉条纹进行拟合剔除。小波分解可在频率域内将轨道误差与形变、大气等其它误差项分离，而具有抗差性的迭代加权最小二乘则使得多项式拟合模型的结果更可靠。分别采用模拟数据和西安地区的EnvisatASAR实际数据分析验证了算法的精度和可靠性。 3）在研究短基线集（Small BAseline Subset，SBAS）时间序列算法和小波多尺度分解（Multiscale InSAR Time Series，MInTS）算法的基础上，针对InSAR时间序列处理技术中存在的相关问题即协方差计算问题，给出了一种融合MInTS和SBAS的综合InSAR时间序列处理算法—MInTS-SBAS算法，该算法既可有效的解决InSAR干涉数据量，以及顾及InSAR数据的相关性的协方差问题，还可对InSAR时间序列处理中的地形、大气等各种误差进行分离。通过西安地区的实际数据，研究表明，本文给出的MInTS-SBAS算法能有效的提高InSAR时序监测结果的精度，与GPS、水准等比较具有更好的一致性和可靠性。 4）针对InSAR时间序列处理中存在的大量时域失相干噪声，给出了一种基于Kalman滤波的InSAR时间序列误差分析方法。研究表明，Kalman滤波算法不仅能对InSAR时序形变中的时域噪声进行有效消除，还可获取优化的线性形变速率值。 5）针对InSAR数据中存在的大量冗余数据，以及强噪声和伪信号等，提出了一种顾及InSAR数据物理空间相关特性设立协方差函数的自适应四叉树分解InSAR数据压缩算法。该算法能够在形变变化明显处进行密集采样，在形变变化缓慢处进行稀疏采样，从而能够在较好的保留InSAR数据的形变细节信息的条件下，达到有效压缩InSAR数据量和消除噪声的目的。 6）以地理信息系统GIS为工具，在对运城市地裂缝灾害形成机理分析的基础上，分别研究了基于层次决策分析法的地裂缝敏感性分析方法和基于BP神经网络模型的地裂缝活动强度预测方法，为运城区域的城市建设和发展提供了必要的地裂缝灾害预警。
[Abstract]:In recent years, with the continuous emission of the new high-resolution SAR satellite and the progress and development of the time-series InSAR technology, InSAR technology has been widely used in the investigation and study of the geological disasters such as earthquake, land subsidence, landslide and debris flow. So as to provide a brand-new dynamic research approach for the fields of geophysical and geodesy and the like, and is a space-to-earth observation new technology with great potential and advantages. However, since the InSAR data is often affected by many errors such as the atmosphere, the DEM, the orbit, and the noise of the distortion, these errors often have the characteristics of multi-source, complexity, cross-cutting and various uncertainties, so that some of the errors in the InSAR monitoring data are difficult to eliminate. In addition, the accuracy and reliability of the InSAR deformation monitoring results are seriously affected, so that the theoretical monitoring precision of the millimeter-level micro-deformation of the surface-resolved unit can not be achieved, the further popularization and the application of the InSAR technology are seriously restricted, Therefore, it is an urgent need to obtain the optimal InSAR monitoring result with high precision and high reliability by the quality control of multiple errors in the InSAR data processing, that is, the analysis of the abnormal data, the coarse difference, the missing or too many redundant information in the InSAR data. It is important to carry out the inversion and early warning of the post-treatment and deformation mechanism of InSAR monitoring. In this paper, based on the statistical analysis of various error characteristics in the InSAR data processing, this paper focuses on the various error problems existing in the InSAR monitoring data, and studies the corresponding error elimination method; and based on the geodesy theory, the paper establishes the mathematical model. The error term in the InSAR data is eliminated by using a reasonable algorithm, so that the high-precision and high-reliability InSAR monitoring data is guaranteed. The following main innovations have been made in this paper through the research The results are as follows:1) Based on the research of the InSAR phase unwrapping method, an InSAR unwrapping phase based on the multi-face function method of the mobile window is proposed for the unwrapping error existing in the InSAR data. Bit-reconstruction model: the multi-surface function method ensures the continuity of the winding phase, and the moving window rule keeps the phase position. In this paper, an InSAR unwrapping phase fitting node determination method, which takes into account the coherence constraint and the characteristic phase, is given in the construction of the model, and the reconstruction model is finally carried out by using the F statistic. Significance test.2) On the basis of the linear fitting estimation method, an anti-difference least square method based on wavelet decomposition is proposed for the residual interference of the track on the basis of the linear fitting estimation method. The wavelet decomposition can separate the orbit error from other error items such as deformation, atmosphere and other errors in the frequency domain, and the iterative weighted least square with the resistance to the difference makes the polynomial fit the model The results of the model are more reliable. The simulation data and the EnvisatAMSAR real data analysis in Xi 'an area are used to validate the algorithm. 3) On the basis of studying the short baseline set (SBAS) time series algorithm and the wavelet multi-scale decomposition (MInTS) algorithm, the relevant questions in the processing technology of the InSAR time series In this paper, an integrated InSAR time series processing algorithm (MInTS-SBAS) based on the MInTS and the SBAS is presented, which can effectively solve the inSAR interference data and the covariance of the correlation between the InSAR data and the terrain and the atmosphere in the processing of the InSAR time series. The results show that the MInTS-SBAS algorithm presented in this paper can effectively improve the accuracy of the InSAR timing monitoring results, compared with the GPS, the level, and so on. good consistency and reliability.4) An InSA based on Kalman filtering is presented for the large amount of time-domain distortion noise present in the processing of the InSAR time series The R-time series error analysis method shows that the Kalman filtering algorithm can not only effectively eliminate the time-domain noise in the time-series deformation of the InSAR, but also can be obtained An adaptive quadtree, which takes into account the physical space-related characteristics of the InSAR data, is proposed to set up the covariance function for the large number of redundant data in the InSAR data, as well as the strong noise and the pseudo-signal. An InSAR data compression algorithm is decomposed. The algorithm can be used for dense sampling at the obvious deformation change, and the sparse sampling is performed at the slow deformation change, so that the effective compression of the InS can be achieved under the condition of better preserving the deformation detail information of the InSAR data. Based on the analysis of the formation mechanism of the crack disaster in the city of Yuncheng, based on the analysis of the formation mechanism of the crack in the city of Yuncheng, the sensitivity analysis method of the ground crack and the BP neural network based on the hierarchical decision-making method are respectively studied on the basis of the analysis of the formation mechanism of the crack in the city. The prediction method of the ground crack activity strength of the complex model, which is the city construction of the Yuncheng area.
【学位授予单位】：长安大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：P225.1;P208
本文编号：2468632
[Abstract]:In recent years, with the continuous emission of the new high-resolution SAR satellite and the progress and development of the time-series InSAR technology, InSAR technology has been widely used in the investigation and study of the geological disasters such as earthquake, land subsidence, landslide and debris flow. So as to provide a brand-new dynamic research approach for the fields of geophysical and geodesy and the like, and is a space-to-earth observation new technology with great potential and advantages. However, since the InSAR data is often affected by many errors such as the atmosphere, the DEM, the orbit, and the noise of the distortion, these errors often have the characteristics of multi-source, complexity, cross-cutting and various uncertainties, so that some of the errors in the InSAR monitoring data are difficult to eliminate. In addition, the accuracy and reliability of the InSAR deformation monitoring results are seriously affected, so that the theoretical monitoring precision of the millimeter-level micro-deformation of the surface-resolved unit can not be achieved, the further popularization and the application of the InSAR technology are seriously restricted, Therefore, it is an urgent need to obtain the optimal InSAR monitoring result with high precision and high reliability by the quality control of multiple errors in the InSAR data processing, that is, the analysis of the abnormal data, the coarse difference, the missing or too many redundant information in the InSAR data. It is important to carry out the inversion and early warning of the post-treatment and deformation mechanism of InSAR monitoring. In this paper, based on the statistical analysis of various error characteristics in the InSAR data processing, this paper focuses on the various error problems existing in the InSAR monitoring data, and studies the corresponding error elimination method; and based on the geodesy theory, the paper establishes the mathematical model. The error term in the InSAR data is eliminated by using a reasonable algorithm, so that the high-precision and high-reliability InSAR monitoring data is guaranteed. The following main innovations have been made in this paper through the research The results are as follows:1) Based on the research of the InSAR phase unwrapping method, an InSAR unwrapping phase based on the multi-face function method of the mobile window is proposed for the unwrapping error existing in the InSAR data. Bit-reconstruction model: the multi-surface function method ensures the continuity of the winding phase, and the moving window rule keeps the phase position. In this paper, an InSAR unwrapping phase fitting node determination method, which takes into account the coherence constraint and the characteristic phase, is given in the construction of the model, and the reconstruction model is finally carried out by using the F statistic. Significance test.2) On the basis of the linear fitting estimation method, an anti-difference least square method based on wavelet decomposition is proposed for the residual interference of the track on the basis of the linear fitting estimation method. The wavelet decomposition can separate the orbit error from other error items such as deformation, atmosphere and other errors in the frequency domain, and the iterative weighted least square with the resistance to the difference makes the polynomial fit the model The results of the model are more reliable. The simulation data and the EnvisatAMSAR real data analysis in Xi 'an area are used to validate the algorithm. 3) On the basis of studying the short baseline set (SBAS) time series algorithm and the wavelet multi-scale decomposition (MInTS) algorithm, the relevant questions in the processing technology of the InSAR time series In this paper, an integrated InSAR time series processing algorithm (MInTS-SBAS) based on the MInTS and the SBAS is presented, which can effectively solve the inSAR interference data and the covariance of the correlation between the InSAR data and the terrain and the atmosphere in the processing of the InSAR time series. The results show that the MInTS-SBAS algorithm presented in this paper can effectively improve the accuracy of the InSAR timing monitoring results, compared with the GPS, the level, and so on. good consistency and reliability.4) An InSA based on Kalman filtering is presented for the large amount of time-domain distortion noise present in the processing of the InSAR time series The R-time series error analysis method shows that the Kalman filtering algorithm can not only effectively eliminate the time-domain noise in the time-series deformation of the InSAR, but also can be obtained An adaptive quadtree, which takes into account the physical space-related characteristics of the InSAR data, is proposed to set up the covariance function for the large number of redundant data in the InSAR data, as well as the strong noise and the pseudo-signal. An InSAR data compression algorithm is decomposed. The algorithm can be used for dense sampling at the obvious deformation change, and the sparse sampling is performed at the slow deformation change, so that the effective compression of the InS can be achieved under the condition of better preserving the deformation detail information of the InSAR data. Based on the analysis of the formation mechanism of the crack disaster in the city of Yuncheng, based on the analysis of the formation mechanism of the crack in the city of Yuncheng, the sensitivity analysis method of the ground crack and the BP neural network based on the hierarchical decision-making method are respectively studied on the basis of the analysis of the formation mechanism of the crack in the city. The prediction method of the ground crack activity strength of the complex model, which is the city construction of the Yuncheng area.
【学位授予单位】：长安大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：P225.1;P208
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