小数据集差分干涉雷达地表形变监测
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摘要
利用差分干涉雷达测量(D-InSAR)技术能快速准确地获取时间序列上高空间分辨率、大区域的地表形变信息。受地表散射体时间变化和SAR平台空间基线去相干的影响,使干涉相位的相干性降低。此外大气在不同观测时间的不均匀波动,使得干涉测量无法完全抵消两次SAR观测时的大气相位延迟,从而在形变测量中增加了大气扰动相位,降低了D-InSAR的测量精度。为了解决上述问题,目前已开发的时序D-InSAR方法大都需要输入大量的SAR数据进行处理。实际上,针对某一监测区域的SAR数据源总是有限的,这一条件限制了现有的时序D-InSAR处理技术应用。
为了在小数据集情况下满足地表形变的监测要求,本文提出一种新的小数据集D-InSAR处理方法。首先利用子孔径分解技术和相干检测的方法,实现小数据集条件下高相干目标检测。针对干涉测量中大气扰动引起的误差问题,利用ICA信号处理的方法进行差分干涉相位的分解,将其中的大气相位信号从测量的干涉相位中剥离。通过上述方法,结合短基线差分干涉思想,确定了一个小数据集D-InSAR处理流程。最后利用统计方法分析了干涉雷达提取的地表形变的分布特征和发展趋势,并将该方法应用于区域性地面沉降监测,同时还尝试了利用该流程提取地下煤火燃烧区的地表形变。结果表明,在对大区域、缓慢形变的地区进行干涉雷达监测,本文提出的方法可以更好地解决小数据集下低相干区地表形变监测问题。此外引入的D-InSAR时空分析方法为地表形变分析提供了另一种实用工具。
本文的创新点：(1)相干系数和子孔径分解相结合提取高相干目标,保证在少量的SAR数据中获取足够多的干涉测量点。(2)使用ICA整体信号分解方法分离大气相位。不通过外部辅助数据,在小数据集的情况下从干涉相位中分离出大气的相位,同时保证了一定的形变提取精度。(3)利用地统计模型分析区域性干涉雷达地面沉降信息的空间自相关情况,提高了形变场时空特征的定量分析能力,比定性方法更容易发现地表形变规律。
High spatial resolution and large area of surface deformation information in time series can be rapidly and accurately obtained by using radar differential interferometry (D-InSAR) technology. However, the coherent of SAR interferogram reduces due to the surface scattering change over time and the long spatial baseline of SAR platform. In addition, the atmospheric delayed phase cannot be fully eliminated due to the uneven atmospheric fluctuations of different observation time, which increases the atmospheric disturbance phase in the deformation measurement and then reduces the measurement accuracy of D-InSAR. In order to solve these problems, the current methods of temporal D-InSAR generally need to input a large number of SAR data. However, the number of SAR data is always limited for a certain area, which also limits the application of the existing low-coherent processing technology.
A new method is proposed to resolve the problem of limited data set. Firstly, high-coherent targets are detected by using sub-aperture decomposition and coherent detection methods from a small data set. Secondly, ICA signal decomposition is adopted to separate atmospheric phase from the measured phase to reduce measurement errors caused by atmospheric disturbances. Thus, by combining these methods with small baseline differential interferometry, a new D-InSAR procedure is developed for a small data set. Finally, the spatial pattern and temporal trend of the extracted surface deformation is analyzed via the spatial-temporal statistical method. And this new procedure is applied to monitor land subsidence in a normal regional area and an underground coal fire deformation area. The results show that when applied to the slow deformation of large area, this method can effectively solve the small data set problem in low coherence of deformation monitoring. Besides, introduced D-InSAR spatio-temporal analysis method provides another useful tool for surface deformation analysis.
The main innovations of this thesis are:(1) the use of coherent coefficients and sub-aperture decomposition to detect reliable high coherent points from small data sets; (2) the use of ICA signal decomposition and gradual approach means separating atmospheric phase from InSAR phase; (3) the space-time surface deformation information extraction by InSAR using geostatistics analysis.
为了在小数据集情况下满足地表形变的监测要求,本文提出一种新的小数据集D-InSAR处理方法。首先利用子孔径分解技术和相干检测的方法,实现小数据集条件下高相干目标检测。针对干涉测量中大气扰动引起的误差问题,利用ICA信号处理的方法进行差分干涉相位的分解,将其中的大气相位信号从测量的干涉相位中剥离。通过上述方法,结合短基线差分干涉思想,确定了一个小数据集D-InSAR处理流程。最后利用统计方法分析了干涉雷达提取的地表形变的分布特征和发展趋势,并将该方法应用于区域性地面沉降监测,同时还尝试了利用该流程提取地下煤火燃烧区的地表形变。结果表明,在对大区域、缓慢形变的地区进行干涉雷达监测,本文提出的方法可以更好地解决小数据集下低相干区地表形变监测问题。此外引入的D-InSAR时空分析方法为地表形变分析提供了另一种实用工具。
本文的创新点：(1)相干系数和子孔径分解相结合提取高相干目标,保证在少量的SAR数据中获取足够多的干涉测量点。(2)使用ICA整体信号分解方法分离大气相位。不通过外部辅助数据,在小数据集的情况下从干涉相位中分离出大气的相位,同时保证了一定的形变提取精度。(3)利用地统计模型分析区域性干涉雷达地面沉降信息的空间自相关情况,提高了形变场时空特征的定量分析能力,比定性方法更容易发现地表形变规律。
High spatial resolution and large area of surface deformation information in time series can be rapidly and accurately obtained by using radar differential interferometry (D-InSAR) technology. However, the coherent of SAR interferogram reduces due to the surface scattering change over time and the long spatial baseline of SAR platform. In addition, the atmospheric delayed phase cannot be fully eliminated due to the uneven atmospheric fluctuations of different observation time, which increases the atmospheric disturbance phase in the deformation measurement and then reduces the measurement accuracy of D-InSAR. In order to solve these problems, the current methods of temporal D-InSAR generally need to input a large number of SAR data. However, the number of SAR data is always limited for a certain area, which also limits the application of the existing low-coherent processing technology.
A new method is proposed to resolve the problem of limited data set. Firstly, high-coherent targets are detected by using sub-aperture decomposition and coherent detection methods from a small data set. Secondly, ICA signal decomposition is adopted to separate atmospheric phase from the measured phase to reduce measurement errors caused by atmospheric disturbances. Thus, by combining these methods with small baseline differential interferometry, a new D-InSAR procedure is developed for a small data set. Finally, the spatial pattern and temporal trend of the extracted surface deformation is analyzed via the spatial-temporal statistical method. And this new procedure is applied to monitor land subsidence in a normal regional area and an underground coal fire deformation area. The results show that when applied to the slow deformation of large area, this method can effectively solve the small data set problem in low coherence of deformation monitoring. Besides, introduced D-InSAR spatio-temporal analysis method provides another useful tool for surface deformation analysis.
The main innovations of this thesis are:(1) the use of coherent coefficients and sub-aperture decomposition to detect reliable high coherent points from small data sets; (2) the use of ICA signal decomposition and gradual approach means separating atmospheric phase from InSAR phase; (3) the space-time surface deformation information extraction by InSAR using geostatistics analysis.
引文
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