Monitoring ground surface large displacements in a 3-D space is important for scientifically understanding and controlling deformation-related geohazards. It has become a promising technique to estimate surface 3-D displacements from airborne multitemporal point cloud datasets using point cloud alignment techniques, such as the typical iterative closest point (ICP) algorithm. However, the typical ICP algorithm aligns point clouds based on geometric features alone, causing a poor robustness in the absence of sufficient topographic structures. This letter presents a new framework so-called weighted hue-based ICP (WHICP) for estimating surface 3-D displacements from multitemporal colored point clouds generated from unmanned aerial vehicle (UAV) stereo photogrammetry. First, a variant of the ICP algorithm named hue-based ICP (HICP) is used, where both geometric and temporally stable color features are used to point cloud alignment. Then, a multiwindow weighted framework is proposed to further process the HICP-aligned point cloud to generate robust estimates of surface 3-D displacements. The WHICP algorithm was tested in a coal mine in Tangshan city, China, where large displacements occurred in flatten terrains. The result shows that the mean accuracy of the WHICP-estimated 3-D displacements is 0.03 m, indicating an improvement by 84% than that of the typical ICP-estimated 3-D displacements.

Phase unwrapping (PU) is a key step in InSAR surface monitoring, which is directly related to the accuracy of the LOS displacement observations. Underground coal mining generally results in large gradient displacements, producing dense and aliasing fringes in interferograms. In this case it is challenging to accurately unwrap interferometric phases using traditional methods. This letter proposes an iterative algorithm to unwrap interferometric phases over underground coal mining areas. Firstly, differential interferogram is unwrapped by the minimum cost flow (MCF) method to obtain unwrapped phases at the edge of the mining subsidence basin. Then, a 2D elliptical Gaussian function, which could approximately describe the spatial shape of mining subsidence basin, is used to iteratively reduce the phase gradients of interferograms, so that residual wrapped phases at more pixel in subsidence basin can be unwrapped. The presented method was tested over Datong coal mine, China, based on 16 TerraSAR-X acquisitions. The result suggests that the accuracy of time-series displacements along the line-of-sight direction solved from the unwrapped phases using the new PU method is 1.1cm, indicating an improvement of 73.2% compared with that of using the traditional MCF method.

Nearly all existing image registration algorithms are based on the full information (e.g., intensity and/or features) carried by the images being registered. Such a full-image-based strategy can achieve high-precision registration (e.g., at the subpixel level) but at the expense of long computation times, especially for high spatial resolution remote-sensing (RS) images. This letter presents a novel subimage-based registration method that is expected to dramatically reduce the time consumption but still meet subpixel accuracy requirements. The core idea behind this method is that the transform pattern of the reference and sensed images under a global transformation is theoretically similar to that of subimages derived from the full image, and vice versa. Hence, this method first divides the original reference image into subimages with nearly equal sizes, and extracts the subimage with the maximum wavelet coefficient summation to serve as a new reference image using the wavelet transform technique. Then, the two-step methods based on the phase correlation and matrix-multiply discrete Fourier transform (DFT) algorithms are applied to register the sensed image to the reference subimage. Finally, the proposed method is tested with simulated data sets. The results indicate that this method can perform subpixel registration as achieved by full-image-based methods but with the benefit of much shorter computation times (e.g., approximately 89% compared with a widely used rapid registration algorithm named enhanced phase correlation).

Accurately predicting ground surface deformation is a crucial task in mining-related geohazards control. Interferometric synthetic aperture radar (InSAR) technique can widely detect historical line-of-sight (LOS) displacements with a high spatial resolution. By incorporating with spatio-temporal deformation models, InSAR can predict kinematic 3-D displacements due to underground mining. However, this method depends on the geometric parameters (at least seven generally) of underground mined-out areas, hindering its practical applications especially over a large area. To circumvent this, we proposed a new method for predicting kinematic 3-D mining displacements by incorporating InSAR with a temporal model, rather than spatio-temporal models used before, in this article. In doing so, much less prior parameters (only three and can be empirically given) are required, with respect to the previous InSAR-based methods. To achieve this, we first revealed that InSAR LOS displacements caused by underground longwall mining at a single point temporally follow an S-shaped growth pattern. Meanwhile, we also showed that a Weibull model can describe the temporal evolution well. Based on these findings, the proposed method first predicts, in a point-wise manner, the kinematic LOS mining displacements from historical InSAR measurements using the Weibull model and a Kalman filter. The kinematic 3-D displacements are then resolved from the predicted LOS displacements with the help of a common prior information relating to mining deformation. The proposed method was tested in the Datong coal mining area of north China. The results show an averaged accuracy of about 0.007 m of the resolved kinematic 3-D displacements.

It is a common method to resolve three-dimensional (3-D) deformation components associated with underground mining by incorporating Single-track interferometric synthetic aperture radar (InSAR) with a prior deformation model termed linear proportion model (LPM) (hereinafter referred to as Sin-LPM). Nevertheless, the Sin-LPM method relies on three model parameters that are needed to bein situcollected, and it neglects their dynamic changes during the period of underground extraction, narrowing the practical applications of the Sin-LPM method, and degrading the accuracy of the estimated 3-D displacements. In this article we propose a new method to resolve 3-D mining displacements from multi-track InSAR observations by incorporating with the LPM. In which, the model parameters are first considered as dynamic and further adaptively estimated from the multi-track InSAR observations using a robust solver. Following that, 3-D mining displacements are resolved from the multi-track InSAR using the conjugate gradient method (CGM). The proposed method was tested in Datong coalfield, China. The results suggest that the proposed method can well estimate 3-D mining displacements with a mean error of about 1.8 cm. Compared with the previous Sin-LPM, the proposed method can effectively improve the accuracy of the estimated 3-D displacements (e.g., 69% in this study), and can work well even over a large area where the model parameters are unknown. The proposed method offers a new insight to improve the InSAR-based retrieval of 3-D displacements induced by other anthropologic or geophysical activities.

This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries' SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications.

Measuring 3-D mining-induced displacements is essential to understand mining deformation mechanisms and assess mining-related geohazards. In our previous work, we proposed a method for estimating 3-D mining-induced large displacements with the surface deformation along the radar line-of-sight (LOS) direction derived from a single amplitude pair (SAP) of synthetic aperture radar (SAR) using the offset tracking (OT) procedure (hereafter referred to as OT-SAP). The OT-SAP method effectively reduces the strict requirements on SAR data of the previous OT-based methods for 3-D mining-induced displacement retrieval. However, OT-SAP is not robust to errors in the LOS deformation, due to the lack of redundant observations. In this paper, we present an alternative approach (hereafter called AOT-SAP) to OT-SAP. The AOT-SAP method involves estimating the 3-D mining-induced large displacements with OT-derived 2-D deformation observations along the LOS and azimuth directions from an SAP of SAR, instead of just the LOS deformation in the OT-SAP method. Consequently, more redundant observations are incorporated in the AOT-SAP method compared with the previous OT-SAP method. The theoretical analysis and experiments based on both simulated and real data sets suggest that AOT-SAP can effectively improve the accuracies of the estimated 3-D displacements compared with the OT-SAP-estimated ones.

Underground extraction can be roughly classified into three types, i.e., subcritical, critical, and supercritical extraction, in accordance with the geological conditions in the overburden and the geometry of mined-out areas. In 2016, we proposed an approach based on the interferometric synthetic aperture radar (InSAR) technique and the probability integral method (PIM) for the cost-effective prediction of 3-D mining-induced displacements (abbreviated as InSAR-PIM). Due to the inherent assumption of critical extraction in the PIM, the InSAR-PIM method performs well in predicting the 3-D displacements caused by critical and/or supercritical extraction, but poorly for subcritical extraction. In this paper, we first propose a generalized PIM (GPIM) by modifying the traditional PIM with a simplified Boltzmann function. We then replace the PIM of the InSAR-PIM with the proposed GPIM to develop an extension of InSAR-PIM (referred as to InSAR-GPIM). The InSAR-GPIM was tested in the Qianyingzi coal mining area, China. The results show that the InSAR-GPIM-predicted horizontal and vertical displacements caused by subcritical, critical, and supercritical extraction agree well with the in situ observations, with average root-mean-square errors of about 0.032 and 0.050 m, respectively. These accuracies represent improvements of 60.9% and 59% when compared with the accuracies predicted by the InSAR-PIM in the horizontal and vertical directions. The results indicate that the InSAR-GPIM is capable of accurately predicting 3-D mining-induced displacements under different extraction conditions (i.e., subcritical, critical, and supercritical extraction), and it performs much better than the InSAR-PIM in the case of subcritical extraction. It is therefore believed that InSAR-GPIM will have a wider scope of applications than the previous InSAR-PIM.

This paper presents a novel method for estimating the model parameters of the probability integral method (PIM) based on the line-of-sight deformation derived from the interferometric synthetic aperture radar. Then, it applies the settled PIM to forward predict the horizontal and vertical displacements induced by the extraction of a new working panel. The proposed method first constructed the functional relationship between the InSAR-derived LOS deformation and the model parameters of PIM. Subsequently, an improved genetic algorithm (GA), in which gross error elimination was imposed, was proposed, and used to estimate the model parameters of PIM with a large number of LOS deformation measurements. The estimated model parameters and PIM were then employed to forward predict the horizontal and vertical displacements induced by the extraction of a working panel. Simulated experiments show that the rmses of the predicted displacements along the up-down, west-east, and north-south directions are 1.5, 0.9, and 2.5 mm, respectively. Real data experiments over the Qianyingzi coal mining area of China indicate that the predicted displacements are highly consistent with those by field surveys, with rmses of 4.1 and 3 cm for the vertical and horizontal directions, respectively. These imply that the proposed approach can be a very promising tool for predicting the mining-induced displacements and will potentially contribute to the assessing and forecasting of possible geological hazards in the mining area.