Abstract
Abstract. The primary step in all timeseries interferometric synthetic aperture radar (T-InSAR) algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases. In areas with a high spatio-temporal deformation gradient, phase unwrapping fails due to the aliasing problem, and so it can result in an underestimation of deformation signal. One way to handle this problem is to use the so called Small-Baseline Subset (SBAS) algorithms; in these algorithms, by using only small-baseline interferograms – hence interferograms with small deformation gradients – the chance of unwrapping error gets reduced. However, due to more number of the used interferograms, SBAS method is computationally more expensive and more time-consuming compared to algorithms that exploit Single-Master (SM) stacks. Moreover, the existence of sufficiently small temporal baseline interferograms is not guaranteed in all SAR stacks. In this paper, we propose a new method to take advantage of short temporal baseline interferograms but effectively using SM approach. We treat the phase unwrapping step as a Bayesian estimation problem while the prior information, required by the Bayesian estimator, is extracted from few short coherent interferograms that are unwrapped separately. Results from the proposed approach and a case study over the southwest of Tehran, with a high subsidence rate (reaching to 25 cm/year), demonstrates that utilizing the proposed method overcomes the aliasing problem and produces the results equal to the conventional SBAS results, while the proposed method is computationally much more efficient than SBAS.
Highlights
KNOWLEDGE ON PHASE UNWRAPPINGThe most crucial processing step in timeseries interferometric synthetic aperture radar (T-InSAR) methodologies is the phase unwrapping
Assuming that deformation is mainly due to the vertical subsidence, as reported by (Haghshenas Haghighi, Motagh, 2019), the estimated maximum deformation is equal to ∼ 17 cm/year vertical subsidence, which as much smaller than ∼ 25 cm/year reported in previous investigations (Motagh et al, 2008, Dehghani et al, 2013) and the recent results obtained by Small-Baseline Subset (SBAS) methodology (Haghshenas Haghighi, Motagh, 2019)
The investigating a coherent interferogram with a five-month temporal baseline shows almost three fringes, equivalent to 8.4 cm line of sight (LOS) deformation in five months
Summary
The most crucial processing step in T-InSAR methodologies is the phase unwrapping. It is crucial because the problem is inherently ill-posed and non-unique. To limit the scope of this study, we mainly focus on T-InSAR algorithms that apply unwrapping in the space domain (i.e. 2D unwrapping), for example in (Hooper, 2006). In these algorithms, the main strategy is to unwrap every interferogram spatially by the minimum cost optimization (Chen, 2001), where cost functions are used to insert the required a-priori knowledge in the unwrapping problem. In the rest of this section, we first recap on the optimization methods for phase unwrapping, following by explanation of common strategies to construct the cost functions
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