A Discontinuity-Guided Two-Dimensional Phase Unwrapping Method for SAR Interferograms

Abstract

Phase unwrapping (PU) is one of the core procedures of interferometric synthetic aperture radar. The traditional PU methods estimate the absolute phase gradient based on the assumption that phase is spatially continuous. However, this assumption is often not true due to the large-phase gradient. In this article, a local enhanced U-net (LEU-Net) was developed to determine the phase discontinuity of synthetic aperture radar (SAR) differential interferograms in areas with substantial elevation change. The phase discontinuity in interferograms predicted by this network was used as a priori information for the phase unwrapping maximum flow\minimum cut algorithm (PUMA). Within the LEU-Net, an interactive compression module was used to reduce the loss of detail information and ensure the ability of the model to segment discrete phase discontinuities; and a cross-fusion module was used to fuse multilevel features and highlight the location information while suppressing noise. The phase discontinuity information predicted by the LEU-Net guided the PUMA to obtain the unwrapped phase through iterative optimization; and in the meantime, the information can suppress the propagation of PU errors from areas of low signal-to-noise areas to areas of high quality. Experiments on simulated and real SAR interferograms with large-phase gradients showed that our method has a more robust PU capability.

Keywords

• Deep learning
• graph cuts
• large gradients
• phase unwrapping (PU)