Abstract
We present a new time series PU approach to improve the unwrapping
accuracy in this article. The rationale behind is to first improve the
sparse unwrapping by mitigating the phase gradient in a 2D network and
then correcting the unwrapping errors in time based on the triplet phase
closure. Rather than commonly-used Delaunay network, we employ
All-Pairs-Shortest-Path (APSP) algorithm in graph theory to maximize the
temporal coherence of all edges and to approach the phase continuity
assumption in the 2D spatial domain. Next, we formulate the PU error
correction in the 1D temporal domain as a compressed sensing (CS)
problem, according to the sparsity rule of remaining phase ambiguity
cycles. We finally estimate phase ambiguity cycles by means of Integer
Linear Programming. The comprehensive comparisons on synthetic and real
Sentinel-1 data covering Lost Hills, California confirm the validity of
the proposed 2D+1D unwrapping approach.