Time Series Phase Unwrapping Based on Graph Theory and Compressed Sensing

Abstract

Time Series SAR interferometry (InSAR) (TS-InSAR) has been widely applied to monitor the crustal deformation with centimeter- to millimeter-level accuracy. Phase unwrapping (PU) errors have proven to be one of the main sources of bias that hinder achieving such high accuracy. In this article, a new time series PU approach is developed to improve the unwrapping accuracy. The rationale behind the proposed method is to first improve the sparse unwrapping by mitigating the phase gradients in a 2-D network and then correcting the unwrapping errors in time, based on the triplet phase closure. Rather than the commonly used Delaunay network, we employ the all-pairs-shortest-path (APSP) algorithm from graph theory to maximize the temporal coherence of all edges and to approach the phase continuity assumption in the 2-D spatial domain. Next, we formulate the PU error correction in the 1-D temporal domain as compressed sensing (CS) problem, according to the sparsity of the remaining phase ambiguity cycles. We finally estimate phase ambiguity cycles by means of integer linear programming (ILP). The comprehensive comparisons using synthetic and real Sentinel-1 data covering Lost Hills, California, confirm the validity of the proposed 2-D + 1-D unwrapping approach and its superior performance compared to previous methods.

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