A Three-Stage Framework for Multi-Baseline InSAR Phase Unwrapping

Abstract

Multi-baseline (MB) phase unwrapping (PU) is an exciting and growing technique in synthetic aperture radar interferometry (InSAR). It can overcome the limitation of the phase continuity assumption in the single-baseline (SB) PU. However, the performance of the MB PU is very sensitive to measurement bias. To overcome the shortness, a three-stage framework (TSF) is proposed, which combines the SB PU technique. In TSF, the interferograms are unwrapped firstly by a SB PU algorithm. Then, we utilize a machine learning algorithm to segment the blocky areas that have the same ambiguity number errors caused by the SB PU errors, and correct the SB PU errors by implementing an edge gradient MB PU technique. In the third stage, a traditional MB PU method is used to estimate the residual global ambiguity number. TSF fuses the SB and MB PU techniques effectively, and the theoretical analysis and experiments indicate that the proposed method can solve the MB PU problem with strong robustness and high computational efficiency.