Phase unwrapping based on the phase‐gradient‐jump connections

2017 ◽  
Vol 53 (10) ◽  
pp. 683-685 ◽  
Author(s):  
Tao Zhang ◽  
Xiaolei Lv ◽  
Jiang Qian ◽  
Jun Hong ◽  
Ye Yun
Keyword(s):  
Phase Unwrapping ◽  
Phase Gradient

Weighted-phase-gradient-based quality maps for two-dimensional quality-guided phase unwrapping

2012 ◽  
Vol 50 (10) ◽  
pp. 1397-1404 ◽  
Author(s):  
Yuangang Lu ◽  
Wancheng Zhao ◽  
Xuping Zhang ◽  
Weihong Xu ◽  
Guoliang Xu
Keyword(s):  
Phase Unwrapping ◽  
Two Dimensional ◽  
Phase Gradient ◽  
Gradient Based ◽  
Quality Maps

scholarly journals Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar

2014 ◽  
Vol 62 (3) ◽  
pp. 511-516 ◽  
Author(s):  
J. Dudczyk ◽  
A. Kawalec
Keyword(s):  
Minimum Cost ◽  
Phase Unwrapping ◽  
Minimum Cost Flow ◽  
Field Methods ◽  
Phase Gradient ◽  
Partial Derivatives ◽  
Flow Algorithm ◽  
Cost Flow ◽  
Derivatives Of ◽  
Branch Cuts

Abstract The last three decades have been abundant in various solutions to the problem of Phase Unwrapping in a SAR radar. Basically, all the existing techniques of Phase Unwrapping are based on the assumption that it is possible to determine discrete ”derivatives” of the unwrapped phase. In this case a discrete derivative of the unwrapped phase means a phase difference (phase gradient) between the adjacent pixels if the absolute value of this difference is less than π. The unwrapped phase can be reconstructed from these discrete derivatives by adding a constant multiple of 2π. These methods differ in that the above hypothesis may be false in some image points. Therefore, discrete derivatives determining the unwrapped phase will be discontinuous, which means they will not form an irrotational vector field. Methods utilising branch-cuts unwrap the phase by summing up specific discrete partial derivatives of the unwrapped phase along a path. Such an approach enables internally cohesive results to be obtained. Possible summing paths are limited by branch-cuts, which must not be intersected. These branch-cuts connect local discontinuities of discrete partial derivatives. The authors of this paper performed parametrization of the Minimum Cost Flow algorithm by changing the parameter determining the size of a tile, into which the input image is divided, and changing the extent of overlapping of two adjacent tiles. It was the basis for determining the optimum (in terms of minimum Phase Unwrapping time) performance of the Minimum Cost Flow algorithm in the aspect of those parameters.

scholarly journals A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

2021 ◽  
Vol 13 (22) ◽  
pp. 4564
Author(s):  
Liming Pu ◽  
Xiaoling Zhang ◽  
Zenan Zhou ◽  
Liang Li ◽  
Liming Zhou ◽  
...  
Keyword(s):  
Least Squares ◽  
Phase Unwrapping ◽  
Gradient Estimation ◽  
Phase Gradient ◽  
Synthetic Aperture Radar Interferometry ◽  
Phase Images ◽  
Robust Least Squares ◽  
Continuity Assumption ◽  
The Difference ◽  
Almost All

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.

Detection of slow-moving landslides in large area using InSAR phase-gradients stacking and YOLOv3

2021 ◽  
Author(s):  
Lv Fu ◽  
Teng Wang
Keyword(s):  
Time Series ◽  
Phase Unwrapping ◽  
Western China ◽  
Landslide Monitoring ◽  
Atmospheric Effects ◽  
Mountainous Area ◽  
Human Society ◽  
Large Area ◽  
Phase Gradient ◽  
Slow Moving

<p>Landslide is one of the major geohazards that endangers the human society and threatens the safety of life and properties. In recent years, attentions have been paid to the Synthetic Aperture Radar interferometry (InSAR) for landslide monitoring with many successful applications. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed in a large area because of phase unwrapping errors, troposphere turbulence and vegetation cover. Here we propose a method combining phase-gradient stacking and the widely-used neural network for tiny object detection: You Only Look Once (YOLOv3) to detect slow-moving landslides from large-scale interferograms. Using the time-series Sentinel-1 SAR images acquired since 2016, we develop a burst-based, phase-gradient stacking algorithm to sum up phase gradients along the azimuth and range directions of short-temporal-baseline interferograms. The stacked phase gradients clearly present the characteristics of localized surface deformation, mainly caused by slow-moving landslides, avoiding the errors result of multiple phase unwrapping in time-series analysis and atmospheric effects. We then train the YOLOv3 network with the stacked phase-gradient maps of known landslides to achieve the quick and automatic landslide detection. We apply our method in the middle section of the Yalong River in mountainous area of western China, with an area of 180,000 km<sup>2</sup>. In addition to the slides that have been published in the inventory, we identify many more slow-moving landslides that cannot be detected by traditional time-series InSAR analysis methods. Our results demonstrate the potential usage of the proposed methods for slow-moving landslide detection in large area, which can be applied before the time-consuming time-series InSAR analysis.</p>

scholarly journals Path-independent phase unwrapping using phase gradient and total-variation (TV) denoising

2012 ◽  
Vol 20 (13) ◽  
pp. 14075 ◽  
Author(s):  
Howard Y. H. Huang ◽  
L. Tian ◽  
Z. Zhang ◽  
Y. Liu ◽  
Z. Chen ◽  
...  
Keyword(s):  
Total Variation ◽  
Phase Unwrapping ◽  
Phase Gradient

scholarly journals One-Step Three-Dimensional Phase Unwrapping Approach Based on Small Baseline Subset Interferograms

2020 ◽  
Vol 12 (9) ◽  
pp. 1473 ◽  
Author(s):  
Christina Esch ◽  
Joël Köhler ◽  
Karlheinz Gutjahr ◽  
Wolf-Dieter Schuh
Keyword(s):  
Spatial Information ◽  
Real Data ◽  
Single Step ◽  
Phase Unwrapping ◽  
Motion Model ◽  
Phase Gradient ◽  
Spatial Phase ◽  
Small Baseline Subset ◽  
Temporal And Spatial ◽  
Small Baseline

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.

Phase unwrapping fitting local planes to phase gradient

2012 ◽  
Author(s):  
E. Gonzalez-Ramirez ◽  
E. de la Rosa Miranda ◽  
L. R. Berriel-Valdos ◽  
Tonatiuh Saucedo Anaya ◽  
Ismael de la Rosa Vargas ◽  
...  
Keyword(s):  
Phase Unwrapping ◽  
Phase Gradient
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