An Improved Method for Phase Triangulation Algorithm Based on the Coherence Matrix Eigen-Decomposition in Time-Series SAR Interferometry

Abstract

Time-series SAR interferometry, which combines permanent scatterers (PSs) and distributed scatterers (DSs), has been strongly developed in recent years. Unlike PS, DS corresponds to a natural target whose neighboring pixels share similar reflectivity values. The selection of DS is relevant to the goodnessof-fit value, the estimation of which is based on all possible combined interferometric phases and fails to avoid the adverse effect of low-quality phases. This paper used eigen-decomposition of coherence matrix that constructed based on the identified homogeneous pixels to perform phase optimization, and then only the interferometric phases with low noise and clear fringes are utilized to measure the goodness-of-fit. 30 Sentinel-1A images were applied to test the improved method of land subsidence monitoring in Beijing, China. The deformation results of different methods were cross verified and the area statistics of the study area were carried out. The results show that the maximum subsidence monitored by the improved method is located in Jinzhan Town with a maximum rate of -109 mm/yr. This improved method extracts more measurement points with accuracy ensured, which is proved to be an effective way to provide the high spatial density of deformation measurements. The land subsidence in this area is mainly caused by the excessive exploitation of groundwater resources. This research provides support for the prevention and control work of relevant departments.

NL-MMSE: A Hybrid Phase Optimization Method in Multimaster Interferogram Stack for DS-InSAR Applications

“…And, a rectangular window is used instead of individual pixels in the sample selection strategy. The eigenvalue decomposition method estimates the coherence matrix of N SAR image based on the SHP of the identified target pixel, and then perform eigenvalue decomposition on the coherence matrix and select the eigenvector with the largest eigenvalue as the temporal optimization value [25]. In addition, considering the results of SHP selection, N SAR images are multiplied by conjugate or weighted conjugate to create a coherence matrix, and the phase is iteratively optimized by maximum likelihood estimation (MLE) theory to improve the interferogram coherence.…”