Improving Differential Interferometry Synthetic Aperture Radar Phase Unwrapping Accuracy with Global Navigation Satellite System Monitoring Data

Hui Wang, Yuxi Cao, Guorui Wang, Peixian Li (), Jia Zhang and Yongfeng Gong
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Hui Wang: NingXia Survey and Monitor Institute of Land and Resources, Yinchuan 750001, China
Yuxi Cao: State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China
Guorui Wang: NingXia Survey and Monitor Institute of Land and Resources, Yinchuan 750001, China
Peixian Li: State Key Laboratory of Coal Mining and Clean Utilization, Beijing 100013, China
Jia Zhang: NingXia Survey and Monitor Institute of Land and Resources, Yinchuan 750001, China
Yongfeng Gong: NingXia Survey and Monitor Institute of Land and Resources, Yinchuan 750001, China

Sustainability, 2023, vol. 15, issue 17, 1-18

Abstract: : We developed a GNSS-assisted InSAR phase unwrapping algorithm for large-deformation DInSAR data processing in coal mining areas. Utilizing the Markov random field (MRF) theory and simulated annealing, the algorithm derived the energy function using MRF theory, Gibbs distribution, and the Hammersley–Clifford theorem. It calculated an image probability ratio and unwrapped the phase through iterative calculations of the initial integer perimeter matrix, interference phase, and weight matrix. Algorithm reliability was confirmed by combining simulated phases with digital elevation model (DEM) data for deconvolution calculations, showing good agreement with real phase-value results (median error: 4.8 × 10 − 4 ). Applied to ALOS-2 data in the Jinfeng mining area, the algorithm transformed interferometric phase into deformation, obtaining simulated deformation by fitting GNSS monitoring data. It effectively solved meter-scale deformation variables between single-period images, particularly for unwrapping problems due to decoherence. To improve calculation speed, a coherence-based threshold was set. Points with high coherence avoided iterative optimization, while points below the threshold underwent iterative optimization (coherence threshold: 0.32). The algorithm achieved a median error of 30.29 mm and a relative error of 2.5% compared to GNSS fitting results, meeting accuracy requirements for mining subsidence monitoring in large mining areas.

Keywords: GNSS; InSAR; mining subsidence monitoring; ALOS-2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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