Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation
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RESEARCH ARTICLE
Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation Meinan Zheng1,2,3 • Kazhong Deng1,2 • Sen Du1,2 • Jie Liu4 • Jiuli Liu4 • Jun Feng5 Received: 30 August 2017 / Accepted: 15 October 2018 Ó Indian Society of Remote Sensing 2018
Abstract 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 timeseries 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 14.2 mm and 43.0 mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE = 16.2 mm, MD = 57.5 mm) and the results of using only the TCPInSAR (RMSE = 26.5 mm, MD = 88.4 mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively. Keywords Probability integral method TCPInSAR Time-series deformation Polynomial model Mining area
Introduction
& Kazhong Deng [email protected] 1
NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China
2
Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou 221116, China
3
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
4
Beijing Institute of Spacecraft System Engineering, Beijing 100094, China
5
Shanxi Province Coal Geology 115 Prospecting Institute, Datong 037003, China
Underground mining can damage roads, bridges, buildings, underground pipelines and other infrastructure located in subsidence basins. Therefore, it is of great theoretical and practical importance to monitor and analyse the spatiotemporal evolution of the whole basin in the mining area to accurately predict and assess any damage to buildings on the s
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