离散优化框架下的多基线 InSAR 相位解缠方法 = A multi-baseline phase unwrapping method based on a discrete optimization framework

Abstract

Multi-baseline phase unwrapping breaks through the limit of phase continuity assumption through extending the ambiguity boundary of single-baseline phase unwrapping. However, phase noise is still challenging the multi-baseline unwrapping. The clustering analysis algorithm can suppress the noise to a certain extent, but it is hard to guarantee continuity of cluster edges. In this paper, a discrete-optimization-based multi-baseline InSAR phase unwrapping algorithm is proposed, which transforms the classical multi-baseline unwrapping into a discrete optimization problem and constructs a multi-baseline unwrapping analytical framework. The method solves the phase ambiguity in the bidirectional form, and introduces block clustering to correct the abrupt change of the phase ambiguities caused by heavy noise, improving the robustness of the algorithm and overcoming the cluster boundary hopping. The effectiveness of the method has been validated through simulation and real data tests. The results show that the proposed algorithm reduces the root mean square error by about 20% compared with the traditional clustering method.