A New 3-D Minimum Cost Flow Phase Unwrapping Algorithm Based on Closure Phase

Liu, Fei; Pan, Bin

doi:10.1109/tgrs.2019.2949926

Cited by 19 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Goldstein's branch-cut (Goldstein) algorithm (Yu et al, 2019) is the most representative of the residue-theorembased methods, connecting the nearby positive and negative residues so that the residues are balanced. The minimum cost flow (MCF) PU method (Costantini, 1998;Liu and Pan, 2020) and Flynn's minimum discontinuity (MD) method (Xu et al, 2016) can be classified as the LP-norm phase unwrapping method. Quality-guided (QD) phase unwrapping method assumes that pixels with high quality are less likely to cause PU error (Yu et al, 2019).…”

Section: Phase Unwrapping For the Clear Interferometric Fringesmentioning

confidence: 99%

“…Phase unwrapping is an important factor affecting the accuracy of InSAR measurement (Wang et al, 2017). Although many methods for InSAR phase unwrapping have been proposed (Chiglia and Pritt, 1998;Costantini, 1998;Xu et al, 2016;Wang et al, 2017;Yu et al, 2019;Luo et al, 2020;Liu and Pan, 2020;Zhou et al, 2020;Sica et al, 2020;Dai et al, 2020;Gao et al, 2020), traditional methods cannot unwrap the interferometric phase correctly and the reliability of InSAR monitoring results is not high in monitoring the large deformation in the mining areas. There are several learning-based phase unwrapping methods, including back-projection neural network in 1D phase unwrapping (Tipper et al, 1996;Hamzah et al, 1997), supervised feedforward multilayer perceptron neural network for 2D phase unwrapping (Schwartzkopf et al, 2000), a deep learning-based phase unwrapping network that uses the fully convolutional network (FCN) (Spoorthi et al, 2019), and also a deep convolutional neural networkbased robust phase gradient estimation for 2D phase unwrapping (Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Wang

Lü

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

show abstract

Section: Phase Unwrapping For the Clear Interferometric Fringesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Wang

Lü

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…Costantini et al formulated the 3-D PU in a common framework [24], which includes standard techniques and constraints from external information if available. Liu et al established a mathematical constraint within the closure phases and unwrapped sparse 3-D grids jointly [25]. However, these methods are sensitive to deformation or atmospheric signal of long time series and sudden changes of residual topography for low density grid, which results in that the phase continuity assumption is not valid any more.…”

Section: Introductionmentioning

confidence: 99%

A Multicomponent Temporal Coherence Model for 3-D Phase Unwrapping in Time-Series InSAR of Seasonal Deformation Areas

Yang

Jiang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

“…Generally, these methods are at the cost of spatial resolution due to the straightforward rectangular estimation window [9]. Additionally, the multilooking process or nonlinear filtering approach may invalidate the phase consistency hypothesis [10], which is also called phase triangularity [11]. However, most coherence models for SAR interferometry purposes implicitly assume phase consistency; otherwise, this would challenge any simple interpretation of the interferometric phase [12].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The multi-temporal interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) has been widely applied in high-precision deformation measurements, which compensates for the drawback that the persistent scatterer InSAR technique does not obtain sufficient monitoring points, especially in rural areas. Considering that DS pixels are susceptible to various decorrelation factors, it is necessary to retrieve the optimal phase series by phase optimization algorithms (POAs). However, conventional POAs rely on a sample covariance matrix or complex coherence matrix (CCM) derived by spatially averaging statistically homogeneous pixel neighborhoods, which may blur and destroy phase information, especially in dense fringe areas. To overcome this limitation, an adaptive POA is proposed in this paper. The adaptive POA artificially constructs a superior CCM by the filtered interferometric phase, which is derived through spatial adaptive filtering approach fusion of principal phase component estimation and fast nonlocal means filtering, and an accurate coherence matrix determined via coherence estimation bias correction. Moreover, the modified eigen-decomposition-based maximum-likelihood-estimator of the interferometric phase (EMI) with coherence-power-weighting is proposed to further improve the estimation precision and computational efficiency. The estimated CCM is then processed with the modified coherence-power-weighted EMI algorithm, and the optimal phase history is retrieved. The experimental results validated against both simulated and Sentinel-1A data demonstrate the superior optimization performance and robustness of the adaptive POA over traditional POAs.

show abstract

A New 3-D Minimum Cost Flow Phase Unwrapping Algorithm Based on Closure Phase

Cited by 19 publications

References 40 publications

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

A Multicomponent Temporal Coherence Model for 3-D Phase Unwrapping in Time-Series InSAR of Seasonal Deformation Areas

An Adaptive Phase Optimization Algorithm for Distributed Scatterer Phase History Retrieval

Contact Info

Product

Resources

About