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

多基线相位解缠技术通过扩展InSAR干涉相位的模糊度区间,突破了单基线解缠的相位连续性限制。然而,相位噪声始终困扰着多基线解缠,聚类分析算法能够在一定程度上抑制噪声,但聚类簇边界的相位连续性难以保障。针对该问题,本文提出了一种离散优化多基线InSAR抗噪相位解缠方法,该方法将经典的多基线解缠模型转化为离散优化问题,构建了多基线解缠解析框架。本文方法以双向遍历的形式求解相位模糊度,针对相位噪声引起的模糊度个数突变问题,采用分块聚类的策略予以校正,提高了算法的抗噪稳健性,解决了聚类簇边界跳变问题。本文基于模拟数据集及实测数据集验证了本文方法的有效性。结果表明,本文方法相对于传统聚类方法,均方根误差减少了约20%。

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.