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A Multicomponent Temporal Coherence Model for 3-D Phase Unwrapping in Time-Series InSAR of Seasonal Deformation Areas. REMOTE SENSING 2022. [DOI: 10.3390/rs14051080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
3-D phase unwrapping (PU) methods based on the 2-D linear temporal coherencemodel have been widely used in time-series interferometric synthetic aperture radar (TS-InSAR) for measuring topography and monitoring subtle deformation. However, the linear temporal coherencemodel can not characterize the coherence of highly coherent pixels accurately in seasonal deformation areas, where nonlinear deformation is deterministic and nonnegligible. Especially, for urban areas with groundwater or thermal dilation seasonal changes or permafrost regions, the nonlinear deformation is usually associated with periodic temperature changes. In this work, a general multi-component temporal coherence model, which considers multiple components including the seasonal deformation, is proposed for 3-D PU of seasonal deformation areas. Moreover, the uncertainty evaluation criterion, based on Cramér–Rao bound (CRB), is derived for TS-InSAR. The experimental results, obtained by applying the multi-component temporal coherence model to a data set acquired from January 2012 to February 2016 over the Beijing Capital International Airport area, confirm the effectiveness of the proposed method. High phase consistency, accurate corrected digital elevation model (DEM) and deformation information monitoring with high-density and high-coverage PS pixels are achieved. Under the same iterations and TS-InSAR procedure, the enhanced performance by the proposed model is illustrated by comparing with that of linear model in terms of phase consistency of 3-D phase unwrapping, PSCs selection at each step, and final results evaluation. In summary, the number of phase-consistency edges after 3-D PU is increased by about 15%, the number of final PS pixels selected with the same coherence threshold constraint is increased by about 10%, and more PS pixels provide a low uncertainty in residual topography, mean deformation velocity and seasonal amplitude estimation.
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A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network. REMOTE SENSING 2021. [DOI: 10.3390/rs13224564] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.
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Bai X, Hancock ER, Wilson RC, Ho TK. Special issue on recent advances in statistical, structural and syntactic pattern recognition. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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