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

Research on Flat Earth Removal Techniques of INSAR

2012 ◽  
Vol 433-440 ◽  
pp. 7487-7492
Author(s):  
Jia Li ◽  
Jia Xin
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Phase Unwrapping ◽  
Radar Interferometry ◽  
Synthetic Aperture ◽  
Imaging Algorithm ◽  
Remote Sensing Technique ◽  
Synthetic Aperture Radar Interferometry ◽  
Removal Technology ◽  
Phase Derivative

As a new microwave remote sensing technique, synthetic aperture interferometry has been developed rapidly in last thirty years. It is based on synthetic aperture. The imaging algorithm of the synthetic aperture radar interferometry (INSAR) is introduced. Two approaches of flat earth removal are derived and compared. The influence of the flat earth removal processing to the interformetry phase statistics distribution and phase derivative is analyzed. The results indicate that the flat earth removal technology makes the phase unwrapping easier and improves the precision of it.

Improved Path-Independent Phase Unwrapping Algorithm Based on Total-Variation Minimum Denoising

2016 ◽  
Vol 53 (12) ◽  
pp. 121202
Author(s):  
张辉钦 Zhang Huiqin ◽  
郭仁慧 Guo Renhui ◽  
蒋超 Jiang Chao ◽  
朱文华 Zhu Wenhua ◽  
周翔 Zhou Xiang
Keyword(s):  
Total Variation ◽  
Phase Unwrapping

Weighted Kalman Filter Phase Unwrapping Algorithm Based on the Phase Derivative Variance Map

2013 ◽  
Vol 475-476 ◽  
pp. 991-995
Author(s):  
Li Fen Wang ◽  
Man Yan
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
State Space Model ◽  
Phase Unwrapping ◽  
Noise Elimination ◽  
Data Application ◽  
Vector Observation ◽  
Phase Derivative ◽  
Steep Terrain ◽  
Error Transfer

The kalman filtering phase unwrapping is a state estimation problem. It can realize phase unwrapping and noise elimination at the same time, and calculate the real phase by establishing the state space model and vector observation model. In the steep terrain, the conventional kalman filtering algorithm unwrapping results are often not accurate, easy to cause the error transfer. Aiming at this problem, the weighted kalman filter phase unwrapping algorithm based on the phase derivative variance map is proposed. The values of the phase derivative variance maps are applied to determine the noise variance in the observation equation, then the weighted kalman filter is used to unwrap phase, this can increase the accuracy of the results. Finally, experiments are carried out in the InSAR data application under the condition of steep terrain, and with the conventional kalman filtering phase unwrapping algorithm are compared, the effectiveness of the proposed method is verified.

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

De-noising interferogram inSAR using variance and absolute deviation functions

2016 ◽  
Vol 13 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Tarek Bentahar ◽  
Djamel Benatia ◽  
Mohamed Boulila
Keyword(s):  
Phase Unwrapping ◽  
Phase Image ◽  
Interferometric Synthetic Aperture Radar ◽  
Absolute Deviation ◽  
Content Type ◽  
Residue Number ◽  
Phase Derivative ◽  
Quality Map ◽  
Better Than

Purpose In this paper, a new efficient method to de-noise the interferometric Synthetic Aperture Radar interferogram, also called wrapped phase image, is proposed with the aim to reduce the residue number and make the phase unwrapping process easy. Design/methodology/approach This method is based on two statistics functions, the former is the phase derivative variance (PDV) defined as a quality map to select the badness areas, the second one is the phase derivative variance (PAD) for a local 3 × 3 pixels filtering which allows to assign an estimated phase for each bad area selected by PDV function. Our filter was tested with a simulated interferograms and compared to other most used filters. Findings With this proposed method, the residues in the interferogram are minimized better than using a conventional filters, and the phase unwrapping process gives a better estimation. Originality/value Combining two statistical functions (PDV and PAD) is efficient in terms of minimizing the noise in the interferogram; this is very helpful to minimize the processing time of the InSAR image particularly the phase unwrapping treatment and have a good quality of the image.

scholarly journals Accuracy improvement of the inSAR quality-guided phase unwrapping based on a modified PDV map.

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Tarek Bentahar
Keyword(s):  
Experimental Test ◽  
Real Data ◽  
Phase Unwrapping ◽  
Accuracy Improvement ◽  
Running Time ◽  
Phase Derivative

In this paper, an accuracy improvement of the quality-guided phase unwrapping algorithm is proposed. Our proposal is based on a modified phase derivative variance which provides more details on local variations especially for important patterns such as fringes and edges, hence distorted regions may be re-unwrapped according to this new reliable PDV. The proposed improvement is not only effective on accuracy but also on time, the obtained results have shown that the running time with our proposal is less than that of a skillful optimization-based algorithm. To prove effectiveness, the experimental test is carried out on simulated and real data, and the comparison is made under several relevant criteria.

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