In the field of Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR), permanent scatterer (PS) is mainly used as the high coherent point, and its selection method widely uses the amplitude dispersion method and the correlation coefficient threshold method. In general, the highly coherent points selected by these two methods can meet the deformation measurement requirements. However, in some areas with complex environments (such as vegetation growth, rainfall and snowfall), the quantity and quality of high-coherent points selected based on these two methods cannot meet the requirements of deformation monitoring. Aiming at the problem that the number of selected highly coherent points is small or the quality is not high in a complex environment, the article proposes an improved highly coherent points detection method: the method uses non-local means filtering to reduce image noise, and then combines the amplitude and phase information of the image with the fuzzy c-means (FCM) clustering algorithm for high-coherent points selection. Finally, through the simulation of measured data and the simulation results show: for the complex environment measured in the article, compared with traditional methods, more high-coherent points that meet the requirements can be selected by using improved methods.
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