Abstract
One of the most critical steps in a multitemporal D-InSAR analysis is the resolution of the phase ambiguities in the context of phase unwrapping. The Extended Minimum Cost Flow approach is one of the potential phase unwrapping algorithms used in the Small Baseline Subset analysis. In a first step, each phase gradient is unwrapped in time using a linear motion model and, in a second step, the spatial phase unwrapping is individually performed for each interferogram. Exploiting the temporal and spatial information is a proven method, but the two-step procedure is not optimal. In this paper, a method is presented which solves both the temporal and spatial phase unwrapping in one single step. This requires some modifications regarding the estimation of the motion model and the choice of the weights. Furthermore, the problem of temporal inconsistency of the data, which occurs with spatially filtered interferograms, must be considered. For this purpose, so called slack variables are inserted. To verify the method, both simulated and real data are used. The test region is the Lower-Rhine-Embayment in the southwest of North Rhine-Westphalia, a very rural region with noisy data. The studies show that the new approach leads to more consistent results, so that the deformation time series of the analyzed pixels can be improved.
Highlights
The aim of this work is the development of a phase unwrapping method that solves the temporal and the spatial phase unwrapping in one single step
An alternative Extended Minimum Cost Flow (EMCF) algorithm is applied, where the motion model parameters are estimated by maximizing the Ensemple Phase Coherence (EPC) function using the modified algorithm and where EPC based weights are used
It can be concluded that a three-dimensional phase unwrapping approach has been successfully defined which solves the temporal and the spatial phase unwrapping in one single step
Summary
The two main limitations of interferometry are the spatial and temporal decorrelation which increases with larger baselines between the two SAR images. To eliminate this effect, PSI uses only so called persistent scatterers whose backscattering characteristics, measured by the amplitude of the returning signal, remain stable over time. PSI uses only so called persistent scatterers whose backscattering characteristics, measured by the amplitude of the returning signal, remain stable over time These pixels are very rare, especially in rural areas. SBAS, on the other hand, reduces the decorrelation effects by only allowing interferograms between SAR images whose spatial and temporal baselines do not exceed a certain threshold. Only pixels with coherence values above a certain threshold are evaluated
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