A Region-growing Phase Unwrapping Approach Based on Local Frequency Estimation for Interferometric SAR

2006 ◽  
Author(s):  
Da-zheng Feng ◽  
Nan Wu ◽  
Baoquan Liu
Keyword(s):  
Frequency Estimation ◽  
Region Growing ◽  
Phase Unwrapping ◽  
Interferometric Sar

SAR Phase Unwrapping Using Region-Growing with Polynomial-Based Phase Prediction

GEOMATICA ◽  
2020 ◽  
Author(s):  
Benjamin Brunson ◽  
Baoxin Hu ◽  
Jianguo Wang
Keyword(s):  
Quality Assurance ◽  
Simulated Data ◽  
Region Growing ◽  
Speckle Noise ◽  
Phase Unwrapping ◽  
Solution Quality ◽  
Northern Ontario ◽  
Statistical Confidence ◽  
Processing Strategies ◽  
Phase Prediction

Phase Unwrapping for Synthetic Aperture RADAR Interferometry (InSAR) remains a challenge due to the speckle noise and temporal decorrelation present in many interferograms. This paper proposes a Polynomial-Based Region-Growing Phase Unwrapping (PBRGPU) approach that builds from the Region-Growing Phase Unwrapping (RGPU) approach developed by Xu and Cumming in 1996 (Xu and Cumming, 1996). This approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, and conducts statistically rigorous quality assurance to identify low quality pixels from further processing. The approach uses a desired statistical confidence interval as its main parameter, which is more intuitive to users than other threshold parameters. The proposed approach is currently the only phase unwrapping approach to take this strategy with its quality assurance. The proposed approach improved upon the solution quality of the RGPU approach, in some cases achieving a tenfold decrease in RMSE for simulated data. Applying the proposed approach to RADARSAT-2 data collected over Polar Bear Provincial Park in Northern Ontario, Canada yielded positive results, and the PBRGPU approach consistently performed on par with or outperformed SNAPHU in terms of solution quality. The PBRGPU approach does lag behind SNAPHU in terms of the domain of the solution, with SNAPHU unwrapping a significantly larger portion of the interferogram in all test cases, but this issue could be mitigated through post-processing the unwrapped interferogram. The proposed approach provides a solid foundation for adaptive region-growing algorithms that integrate all available information rather than relying on pre-processing strategies.

A region-growing technique to improve multi-temporal DInSAR interferogram phase unwrapping performance

2013 ◽  
Vol 4 (10) ◽  
pp. 988-997 ◽  
Author(s):  
Y. Yang ◽  
A. Pepe ◽  
M. Manzo ◽  
F. Casu ◽  
R. Lanari
Keyword(s):  
Region Growing ◽  
Phase Unwrapping ◽  
Multi Temporal

Two-look roll compensation for aircraft-borne interferometric SAR without phase unwrapping

2005 ◽  
Vol 41 (6) ◽  
pp. 367 ◽  
Author(s):  
R.J. Bullock ◽  
P.V. Brennan ◽  
H.D. Griffiths
Keyword(s):  
Phase Unwrapping ◽  
Interferometric Sar

Multichannel interferometric SAR phase unwrapping using extended Kalman Smoother

2013 ◽  
Vol 5 (3) ◽  
pp. 429-436 ◽  
Author(s):  
Davide Chirico ◽  
Gilda Schirinzi
Keyword(s):  
Phase Unwrapping ◽  
Sensor Data ◽  
Bayesian Estimator ◽  
Bayesian Approaches ◽  
Strong Discontinuities ◽  
Absolute Phase ◽  
Multi Sensor Data Fusion ◽  
Digital Elevation ◽  
Elevation Model ◽  
Interferometric Sar

Phase unwrapping (PU) is one of the key processing steps in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from observation of its noisy principal (wrapped) values. Recently, PU approaches based on Kalman filtering have proved their efficacy in tackling the PU problem even when strong discontinuities of the height profile and noisy data are involved. This paper presents a novel multichannel InSAR PU algorithm using several interferometric SAR images based on the extended Kalman filter. The proposed technique exploits the capability of the Kalman algorithm to simultaneously perform noise filtering, PU, and multi-sensor data fusion. The proposed method, even being a Bayesian estimator, optimally fuses height information coming from an additional maximum likelihood estimator (MLE) combining the benefits of both the Bayesian and the non-Bayesian approaches. The performance of the proposed algorithm has been tested on simulated interferometric images proving the effectiveness of the proposed method.

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