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

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

Path-independent phase unwrapping using phase derivative and total-variation (TV) denoising

2012 ◽  
Author(s):  
Howard Y. H. Huang ◽  
L. Tian ◽  
Z. Zhang ◽  
Y. Liu ◽  
G. Barbastathis
Keyword(s):  
Total Variation ◽  
Phase Unwrapping ◽  
Phase Derivative

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.

Sign in / Sign up

Export Citation Format

Share Document