scholarly journals Improving the Robustness of the MTI-Estimated Mining-Induced 3D Time-Series Displacements with a Logistic Model

2021 ◽  
Vol 13 (18) ◽  
pp. 3782
Author(s):  
Jiancun Shi ◽  
Zefa Yang ◽  
Lixin Wu ◽  
Siyu Qiao
Keyword(s):  
Temporal Resolution ◽  
Logistic Model ◽  
Three Dimensional ◽  
Mining Area ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Prior Model ◽  
Displacement Estimation ◽  
Coal Mining Area ◽  
Surface Displacements

The previous multi-track InSAR (MTI) method can be used to retrieve mining-induced three-dimensional (3D) surface displacements with high spatial–temporal resolution by incorporating multi-track interferometric synthetic aperture radar (InSAR) observations with a prior model. However, due to the track-by-track strategy used in the previous MTI method, no redundant observations are provided to estimate 3D displacements, causing poor robustness and further degrading the accuracy of the 3D displacement estimation. This study presents an improved MTI method to significantly improve the robustness of the 3D mining displacements derived by the previous MTI method. In this new method, a fused-track strategy, instead of the previous track-by-track one, is proposed to process the multi-track InSAR measurements by introducing a logistic model. In doing so, redundant observations are generated and further incorporated into the prior model to solve 3D displacements. The improved MTI method was tested on the Datong coal mining area, China, with Sentinel-1 InSAR datasets from three tracks. The results show that the 3D mining displacements estimated by the improved MTI method had the same spatial–temporal resolution as those estimated by the previous MTI method and about 33.5% better accuracy. The more accurate 3D displacements retrieved from the improved MTI method can offer better data for scientifically understanding the mechanism of mining deformation and assessing mining-related geohazards.

scholarly journals A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiyong Wang ◽  
Lu Li ◽  
Yaran Yu ◽  
Jian Wang ◽  
Zhenjin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Land Subsidence ◽  
Mining Area ◽  
Phase Unwrapping ◽  
New Method ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Coal Mining Area ◽  
Low Coherence

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 neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

scholarly journals Simulation of Three-Dimensional of coastal erosion using differential interferometric synthetic aperture radar

2013 ◽  
Vol 16 (1) ◽  
pp. 80-86
Keyword(s):  
Synthetic Aperture Radar ◽  
Three Dimensional ◽  
Shoreline Change ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Envisat Asar ◽  
Digital Elevation ◽  
Elevation Model ◽  
Dimensional Object ◽  
Aperture Radar

<p>This study aims at modelling three-dimensional shoreline change rates using differential interferometric synthetic aperture radar (DInSAR) techinuqe. Neverthless, decorrelation plays significant role to control the accuracy of three dimensional object reconstruction using DInSAR. To solve this problem, multichannel MAP height estimator algorithm is implemented with in ENVISAT ASAR data. Therefore, the proposed method has been applied to coastaline of Johor, Malaysia. The study shows the critical erosion of -3.5 m y-1 with accuracy (RMSE) of &plusmn;0.05 m. In addition, the volume rate of shoreline changes of -2343.42 m3 y-1 corresponds to the lowest digital elevation model (DEM) of 7.4 m. It can be said that accurate rate of shoreline change can be achieved with root mean square error (RMSE) of &plusmn;0.05 m using multichannel MAP height estimator algorithm.</p>

A deep learning approach for efficient multi-temporal interferometric synthetic aperture radar (MT-InSAR) processing

2021 ◽  
Author(s):  
Ashutosh Tiwari ◽  
Avadh BIhari Narayan ◽  
Onkar Dikshit
Keyword(s):  
Deep Learning ◽  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Displacement Estimation ◽  
Interferometric Phase ◽  
Selection Step ◽  
Multi Temporal ◽  
Aperture Radar

&lt;p&gt;Multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique has been effectively used to monitor deformation events over the last two decades. The processing steps generally involve pixel selection, phase unwrapping and displacement estimation. The pixel selection step takes most of the processing time, while a reliable method for phase unwrapping is still not available. This study demonstrates the effect of using deep learning (DL) architectures for MT-InSAR processing. The architectures are applied to reduce time computations and further to improve the quality of pixel selection. Some promising results for pixel selection have been shown earlier with the proposed architecture. In this study, we investigate the performance of the proposed architectures on newer datasets with larger temporal interval. To achieve this objective, the models are retrained with interferometric stacks covering larger temporal period and large time steps (for better estimation of interferometric phase components). Pixel selection results are compared with those obtained using open access algorithms used for MT-InSAR processing.&lt;/p&gt;

scholarly journals Application of atmospheric correction in the measurement of land displacements using the PSInSAR technique, on the example of the Tahmoor mining region, Australia

2018 ◽  
Vol 71 ◽  
pp. 00021
Author(s):  
Anna Kopeć
Keyword(s):  
Atmospheric Correction ◽  
Mining Area ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Signal Phase ◽  
Good Tool ◽  
Weather Model ◽  
Eastern Australia ◽  
High Dynamics ◽  
The Impact

InSAR (Interferometric Synthetic Aperture Radar) techniques are a very good tool for identification and observation of surface area displacements. Achieved accuracy of several centimeters, still do not allow for the quantitative analysis of the observed movements. Due to the high dynamics of phenomena in the Earth's atmosphere, one of the biggest limitations of InSAR are disturbances caused by changes in the atmosphere, between two measurements, on the basis of which an interferogram is created. In order to reduce the impact of the atmosphere on the SAR signal course, an atmospheric correction is applied.This study presents the results of calculations using the PSInSAR technique for the Tahmoor mining area located in south-eastern Australia, from 2006-2010. The atmospheric correction was determined: in an empirical way - on the basis of a linear relation between the signal phase and the topography of the area, based on data from the ERA - Interim weather model and data from the MERIS spectrometer.

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