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