Comparison of optimization algorithms for interferometric synthetic aperture radar phase unwrapping based on identical Markov random fields

摘要

Phase unwrapping (PU) is one of the key processes in measuring the elevation or deformation of the Earth's surface from its interferometric synthetic aperture radar (InSAR) data. PU problems may be formulated as maximum a posteriori estimation estimations of Markov random field (MRF). The key issue of this formulation is energy minimization. Iterated conditional mode (ICM), graph cuts (GC), loopy belief propagation (LBP), and sequential tree-reweighted message passing (TRW-S) have been proposed for the energy minimization. Unfortunately, they differ in the formulation of the MRF model for PU, which raises the question of how they compare against each other on the same MRF model for PU. We address this by investigating the four optimization algorithms and comparing them on an identical MRF model, which gives researchers some guidance as to which optimization method is best suited for solving the PU problem. Experiments using simulated and real-data illustrate that the GC algorithm is clearly the winner among the four algorithms in all cases. The ICM algorithm, although very rapid, performs much worse than the other three especially in the terrain with violent changes or discontinuities. The two message-passing algorithms-LBP and TRW-S-perform completely differently. The LBP algorithm performs surprisingly poorly on solving phase discontinuities issue, whereas the TRW-S algorithm does quite well (second only to the GC algorithm). (C) The Authors.