Monitoring the surface deformation of filling coal mines regularly and understanding their spatiotemporal evolution characteristics for mining management and disaster warning is greatly significant. However, there is a lack of research on extracting spatiotemporal evolution characteristics of large-scale and high-resolution surface deformation in backfill mining areas and on evaluating filling effectiveness of subsidence restraint. In this study, we took the Yineng Coal Mine in Shandong Province of China and the surrounding area of the coal mine as the study area. The advanced Distributed scatterer InSAR (DS InSAR) technique was adopted for time-series analysis. The probability integral method (PIM) model for backfilling mining and an arctangent time function were integrated with DS InSAR to overcome the sparsity of InSAR observation points due to the temporal decorrelation caused by vegetation coverage. The results show that the proposed integration strategy is helpful in improving the number of effective monitoring points and obtaining the complete spatiotemporal information of the surface deformation of the working face. The whole study area has eight key deformation zones. All six working faces in the Yineng initial minery display different degrees of deformation during the study period. The comparison between the deformation results of backfill mining and the simulated deformation of nonbackfill mining in the CG1312 working face shows that backfilling mining technology effectively reduces the range (22.00%) and magnitude (61.50%) of surface subsidence and significantly reduces the potential threat to surface buildings.

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

The multitemporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this article. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unscented kalman filter (AUKF), an efficient quality-guided strategy based on heapsort, and a circular median filter is proposed. PU theory and the existing UKFPU method are covered. Then, the improved method is presented with emphasis on the AUKF and the circular median filter. AUKF has been well used in other fields, but it is for the first time applied to interferometric images PU, to the best of our knowledge. First, the amended matrix pencil model is used to estimate the phase gradient. Then, an AUKF model is used to unwrap the interferometric phase based on an efficient quality-guided strategy based on heapsort. Finally, the key results are obtained by filtering the results using a circular median. The proposed method is compared with the minimum cost network flow (MCF), statistical cost network flow (SNAPHU), regularized phase tracking technique (RPTPU), and UKFPU methods using two sets of simulated data and two sets of experimental GF-3 SAR data. The improved method is shown to yield the greatest accuracy in the interferometric phase maps compared to the methods considered in this paper. Furthermore, the improved method is shown to be the most robust to noise and is thus most suitable for PU of GF-3 SAR data in high-noise and low-coherence regions.

2-D phase unwrapping (PU) is one of the key steps in the processing of interferometric synthetic aperture radar (InSAR) data. PU in large-gradient areas has always been a fundamental problem. The phase continuity assumption makes it difficult to obtain ideal PU results in large-gradient areas. To address this issue, in this letter, we propose a phase slicing 2-D PU method using the L ¹ -norm that can effectively reduce the phase gradient interval. First, the study area is analyzed using an external digital elevation model (DEM) and interferometric phase information to obtain the number of phase slices and assisted phases. Then, we cut the interferometric phase to obtain the sliced interferometric phase and use the single-baseline (SB) L ¹ -norm PU method to unwrap the sliced interferometric phase. Finally, the unwrapped phase is recovered by summing the sliced unwrapped phases and assisted phases. Experiments are conducted with simulated and TanDEM-X InSAR data sets. Compared with the representative SB-PU method, the proposed method can effectively avoid the problem of large gradients in PU.