Real-time Selection Method of Time Series GB-InSAR Image PS Based on K-means Algorithm

GB-InSAR (Ground-based Interferometric Synthetic Aperture Radar) is a new type of deformation measurement tool. When applied to long-term continuous monitoring, in order to ensure the accuracy and real-time of the deformation measurement, it is usually necessary to group SAR images and realize deformation inversion based on PS (Permanent Scatterer) technology. The analysis results of the long-term SAR image sequence show that the amplitude of the pixel points will change greatly over time, resulting in the use of a fixed amplitude deviation threshold to select PS points, the number of PS points in each group will change greatly and seriously affect the accuracy of deformation measurement. This paper proposes a real-time selection method of time series GB-InSAR image PS based on K-Means (K-means) clustering. First, the time series images are grouped, and then the pixel amplitude and correlation coefficient of each group of images are used, based on K-Means The algorithm performs two-level clustering. The analysis results of the measured data of an open-pit mine show that the method in this paper ensures that the PS points of each group of images are enough, and the stability of the number of PS points is also greatly improved.