scholarly journals Improved weighted least-squares phase unwrapping method for interferometric SAR processing

2019 ◽  
Vol 2019 (20) ◽  
pp. 6471-6474 ◽  
Author(s):  
Yu Hui ◽  
Wang Wenying ◽  
Zhuang Long ◽  
Lei Wanming ◽  
Nie Xin ◽  
...  
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Interferometric Sar ◽  
Sar Processing

scholarly journals A Robust Discontinuous Phase Unwrapping Based on Least-Squares Orientation Estimator

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2871
Author(s):  
Gaoxu Deng ◽  
Shiqian Wu ◽  
Shiyang Zhou ◽  
Bin Chen ◽  
Yucheng Liao
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Bilateral Filter ◽  
Phase Unwrapping ◽  
Noise Variance ◽  
Post Processing ◽  
Quadratic Error ◽  
Error Norm ◽  
Discontinuous Phase ◽  
Simulation Results

Weighted least-squares (WLS) phase unwrapping is widely used in optical engineering. However, this technique still has issues in coping with discontinuity as well as noise. In this paper, a new WLS phase unwrapping algorithm based on the least-squares orientation estimator (LSOE) is proposed to improve phase unwrapping robustness. Specifically, the proposed LSOE employs a quadratic error norm to constrain the distance between gradients and orientation vectors. The estimated orientation is then used to indicate the wrapped phase quality, which is in terms of a weight mask. The weight mask is calculated by post-processing, including a bilateral filter, STDS, and numerical relabeling. Simulation results show that the proposed method can work in a scenario in which the noise variance is 1.5. Comparisons with the four WLS phase unwrapping methods indicate that the proposed method provides the best accuracy in terms of segmentation mean error under the noisy patterns.

scholarly journals On the Use of Weighted Least-Squares Approaches for Differential Interferometric SAR Analyses: The Weighted Adaptive Variable-lEngth (WAVE) Technique

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1103
Author(s):  
Francesco Falabella ◽  
Carmine Serio ◽  
Giovanni Zeni ◽  
Antonio Pepe
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Variable Length ◽  
Mathematical Framework ◽  
Ground Displacement ◽  
Phase Measurements ◽  
Temporal Sampling ◽  
Wave Technique ◽  
Interferometric Sar

This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the “good” interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided.

Weighted least squares phase unwrapping based on the wavelet transform

2007 ◽  
Author(s):  
Jiafeng Chen ◽  
Haiqin Chen ◽  
Zhengang Yang ◽  
Haixia Ren
Keyword(s):  
Wavelet Transform ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping

Least Squares Phase Unwrapping Algorithm Based on Topographic Factors

2011 ◽  
Vol 105-107 ◽  
pp. 1876-1879
Author(s):  
Wei Ke Liu ◽  
Gou Lin Liu ◽  
Xiao Qing Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping ◽  
Local Phase ◽  
Complex Signals ◽  
Topographic Factors ◽  
Interference Fringes ◽  
Phase Images ◽  
New Phase ◽  
Accuracy And Stability

The phase of complex signals is wrapped since it can only be measured modulo-2; unwrapping searches for the 2-combinations that minimize the discontinuity of the unwrapped phase, as only the unwrapped phase can be analyzed and interpreted by further processing. Weighted least squares phase unwrapping algorithm could avoid errors transmission in the whole phase images, but it could not avoid defect and overlay of interference fringes caused by topographic factors. Therefore, a new phase unwrapping and weights choosing method based on local phase frequency estimate of topographic factors was presented. Experiments show it is an efficient phase unwrapping method which well overcome the defect of under-estimate slopes by least squares algorithm, and has higher accuracy and stability than other methods.

Weighted least-squares phase unwrapping algorithm based on a non-interfering image of an object

2017 ◽  
Vol 56 (15) ◽  
pp. 4543 ◽  
Author(s):  
Xian Wang ◽  
Suping Fang ◽  
Xindong Zhu
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Phase Unwrapping
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