A distributed scatterers InSAR method based on adaptive window with statistically homogeneous pixel selection for mining subsidence monitoring

Statistically homogeneous pixels (SHP) selection is one of the primary steps in time-series InSAR technologies based on distributed scatterers. However, using a fixed window to extract SHP in mining areas may fail to effectively protect and restore the deformation phase in subsequent phase optimization. Therefore, we propose a method based on iterative adaptive window to extract SHP. This approach uses the variation coefficient to judge the intensity of fringes to realize the adaptive window adjustment in and outside the subsidence areas based on different ranges. Moreover, we use eigenvalue decomposition to optimize the phase, and use small baseline subset (SBAS) interferometry to perform time-series modelling and deformation calculations for the selected high-coherence points. The reliability of this method is verified from comparisons with leveling data. Compared with traditional time-series InSAR methods, the proposed method significantly improves the monitoring point density, deformation accuracy and has broad prospects in mining subsidence monitoring.

Automated summary

The proposed method introduces an iterative adaptive window approach for extracting SHP, effectively protecting and restoring deformation phase, and holding great promise for mining subsidence monitoring.