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首页> 外文期刊>Journal of the Indian Society of Remote Sensing >Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation
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Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation

机译:用于监测采矿时间序列变形的联合概率积分法和TCPinsar

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摘要

Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing deformation information, making the obtained surface time-series deformation incomplete. To this end, this paper proposes a way of using the results predicted by probability integral method (PIM) to replacing the monitoring results that cannot be obtained because of the seriously incoherent SAR interferograms; then, the monitoring results of the high-coherence SAR interferograms and the results predicted by PIM are used by the improved temporarily coherent point SAR interferometry (TCPInSAR) to invert the deformation, thereby obtaining a complete mining time-series deformation. The TCPInSAR using a linear model does not reflect the complex deformation characteristics of the mining area. So this paper focus on the characteristics of deformation of study area, the original linear model is changed to a polynomial model, which improves the applicability of TCPInSAR to monitoring mine deformation. Comparison between the experimental results and levelling shows that the root mean square error (RMSE) and the maximum deviation (MD) of the results obtained by combining the PIM with the improved TCPInSAR are 14.2mm and 43.0mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE=16.2mm, MD=57.5mm) and the results of using only the TCPInSAR (RMSE=26.5mm, MD=88.4mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively.
机译:由于植被覆盖率高,某些矿区的快速变形,一些SAR干涉图严重不连贯。当使用时间序列合成孔径雷达干涉测定法(INSAR)监测挖掘区域的表面移动盆时,可能存在一定时期的缺失变形信息,使得所获得的表面时间序列变形不完整。为此,本文提出了一种使用概率积分法(PIM)预测的结果来取代无法获得的监测结果,因为严重的SAR干涉图;然后,通过改进的临时相干点SAR干涉测定法(TCPINSAR)来使用PIM的高相干SAR干涉图的监测结果和PIM预测的结果以反转变形,从而获得完整的采矿时间序列变形。使用线性模型的TCPinsar不反映矿区的复杂变形特性。因此,本文侧重于研究区域变形的特点,原始线性模型改变为多项式模型,这提高了TCPinsar对监测矿井变形的适用性。实验结果和调平之间的比较表明,通过将PIM与改进的TCPinsar组合获得的结果的根均方误差(RMSE)和最大偏差(MD)分别为14.2mm和43.0mm。与通过将PIM与TCPINSAR(RMSE = 16.2mm,MD = 57.5mm)组合而获得的结果相比,仅使用TCPINSAR(RMSE = 26.5mm,MD = 88.4mm),监测精度增加12.3 %和46.4%。

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