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Open AccessArticle

On the Analysis of the Phase Unwrapping Process in a D-InSAR Stack with Special Focus on the Estimation of a Motion Model

1
Institute of Geodesy and Geoinformation, University of Bonn, 53115 Bonn, Germany
2
Joanneum Research, DIGITAL Institute for Information and Communication Technologies, 8010 Graz, Austria
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2295; https://doi.org/10.3390/rs11192295
Received: 26 July 2019 / Revised: 21 September 2019 / Accepted: 28 September 2019 / Published: 1 October 2019
(This article belongs to the Section Environmental Remote Sensing)
This paper analyses the critical phase unwrapping step in a differential interferometric phase (D-InSAR) stack where both the solving of conventional methods and alternative approaches are discussed. It can be shown that including the temporal relationship between interferograms in the phase unwrapping step improves the results. This leads to the three-dimensional extended minimum cost flow algorithm. To unwrap the phase in a multitemporal way a motion model has to be considered. The estimation of these parameters is an important step. By default, the parameters are estimated in an iterative search process, where in each step, a linear program has to be solved. The best parameters are defined by the minimal costs. Often the choice of this search space is not straightforward. Furthermore, with this discrete optimization function, the solution is often not unique. This paper presents an alternative way to estimate the motion model parameters by maximizing a continuous function, the ensemble phase coherence. With the help of a closed-loop simulation and real data, both methods, the standard and the alternative way, are numerically compared and analyzed. Consequently, it is shown that maximizing the ensemble phase coherence is a good alternative to the established iterative procedure. It offers the advantage that the run time can be reduced considerably and is thus well suited in the processing of large data sets. View Full-Text
Keywords: D-InSAR; SBAS; phase unwrapping; minimum cost flow; extended minimum cost flow; simulated annealing D-InSAR; SBAS; phase unwrapping; minimum cost flow; extended minimum cost flow; simulated annealing
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MDPI and ACS Style

Esch, C.; Köhler, J.; Gutjahr, K.; Schuh, W.-D. On the Analysis of the Phase Unwrapping Process in a D-InSAR Stack with Special Focus on the Estimation of a Motion Model. Remote Sens. 2019, 11, 2295.

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