Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness.

No abstract

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 Manuscript

No abstract