Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

• Published in: Geomatica
• Main Authors: Brunson, Benjamin, Hu, Baoxin, Wang, Jianguo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Canadian Science Publishing 2020
• Subjects: Earth-Surface Processes; Geography, Planning and Development; Canada; Polar Bear Provincial Park; Polar bear provincial park
• Online Access: http://dx.doi.org/10.1139/geomat-2020-0013; https://cdnsciencepub.com/doi/full-xml/10.1139/geomat-2020-0013; https://cdnsciencepub.com/doi/pdf/10.1139/geomat-2020-0013

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed approach is currently the only phase unwrapping approach to take this strategy with its quality assurance. The proposed approach was found to improve upon the solution quality of the RGPU approach, in some cases achieving a tenfold decrease in root-mean-square error for simulated data. The PBRGPU approach performed well when applied to RADARSAT-2 data collected over Polar Bear Provincial Park (Ontario, Canada). The PBRGPU solutions were consistently on par with or superior to those generated by SNAPHU in terms of accuracy. While 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, this issue could readily be mitigated through post-processing of the unwrapped interferogram. The proposed approach provides a solid foundation for region-growing algorithms that adapt to local noise levels and integrate all available information rather than relying on preprocessing strategies.