A NEW PHASE UNWRAPPING METHOD COMBINING MINIMUM COST FLOW WITH DEEP LEARNING

Phase unwrapping is a crucial step of InSAR, and its reliability directly determines the feasibility of deformation monitoring. However, severe noise and dense fringes often make the existing unwrapping methods fail. In this work, we propose a convolutional neural network DENet for identifying phase discontinuities and design a data set simulation strategy to generate enough training samples. We combine the traditional cost flow method with the output from DENet to achieve more accurate phase unwrapping. Compared with the GAMMA, the root mean square error of the proposed method on the simulated data set is reduced by 46.4%. We also verified the superior performance of the proposed method on real data sets.