LaMIE: Large-Dimensional Multipass InSAR Phase Estimation for Distributed Scatterers

State-of-the-art phase linking (PL) methods for distributed scatterer interferometry (DSI) retrieve consistent phase histories from the sample coherence matrix or the one whose magnitudes are calibrated. To unify them, we first propose a framework consisting of sample coherence matrix estimation and Kullback–Leibler (KL) divergence minimization. Within such framework, we observe that the current state-of-the-art PL methods mainly focus on calibrating the magnitudes of sample coherence matrix while ignoring the errors caused by it exploited in the complex domain, especially when the PL problem is large-dimensional. In this paper, “large-dimensional” refers to the case where the temporal dimension N of coherence matrices and the number P of statistically homogeneous pixels (SHP) are at the same level. To solve this issue, we further propose a PL method, termed LaMIE, which is aimed at precise phase history retrieval from large-dimensional coherence matrices for DSI. It includes two steps: 1) sample coherence matrix shrinkage to calibrate the matrix in complex and real domains and 2) phase history retrieval via the flat coherence metric. Both simulated and real data experiments validate the effectiveness of the proposed method by comparing it with other PL methods. Through LaMIE, the densities of the selected points with stable phases can be significantly improved, and the displacement velocities for more regions can be obtained than with state-of-the-art methods.