Two-dimensional phase unwrapping method based on improved particle swarm optimization

Branch-cut is one of the most important approaches for the phase unwrapping, which is a key process for the data elevation in the interferometric synthetic aperture radar(InSAR).Generally,the shorter the length of the branch-cut, the better the result of phase unwrapping.Therefore,how to determine the length of the branch cut becomes very important in this kind of branch-cut-based approaches. In this paper,a novel method for optimizing the length of the branch-cut is presented based on particle swarm optimization(PSO)algorithm,which has already been successfully applied to find the shortest path in the traveling salesman problem (TSP)theory.In order to remedy the local convergence problem,the mutation operator of the genetic algorithm is further introduced and combined with the PSO algorithm. Compared to the branch-cut of Goldstein,this method can effectively reduce the length of branch-cut,and also avoid the "isolated island phenomenon" in the unwrapping process.Finally,the effectiveness and feasibility of the proposed method have been verified through both the simulated and experimental data.