2021
DOI: 10.3389/feart.2021.761653
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A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Abstract: Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional… Show more

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Cited by 4 publications
(3 citation statements)
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“…In the last years, convolutional neural networks emerged as a promising tool in subsidence analysis, supporting phase unwrapping and extraction of interferometric fringes (Wang et al, 2021b) and the development of an effective system for the detection of subsidence troughs in SAR interferograms (Rotter and Muron, 2021).…”
Section: Technical Papermentioning
confidence: 99%
“…In the last years, convolutional neural networks emerged as a promising tool in subsidence analysis, supporting phase unwrapping and extraction of interferometric fringes (Wang et al, 2021b) and the development of an effective system for the detection of subsidence troughs in SAR interferograms (Rotter and Muron, 2021).…”
Section: Technical Papermentioning
confidence: 99%
“…Large-scale land subsidence resulting from coal mining has caused a series of ecological and environmental problems, including destroying farmlands, damaging buildings, and even inducing geological disasters (Wang et al, 2021a;Yuan et al, 2021). It threatens the lives and property of the local residents and restricts the economic sustainable development in mining areas (Fan et al, 2018;Wang et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…However, in the long-term monitoring, InSAR is limited by signal incoherence, which is caused by atmospheric phase error and noise [13], resulting in reduced monitoring accuracy and a loss of ability to monitor tiny deformations. In view of this problem, several scholars improved the phase filtering [14] and phase unwrapping [15] methods in InSAR or established a refined model for a single subsidence basin [16] to improve the monitoring accuracy. In 2002, Small Baseline Subset InSAR (SBAS-InSAR) was proposed by Berardino et al [17].…”
Section: Introductionmentioning
confidence: 99%