A Wrap-Count-Based Phase Unwrapping Method for Large-Scale, Low-Coherence Interferograms Using Deep Learning (opens in new tab)

Unwrapping synthetic aperture radar interferograms with extensive low-coherence regions remains challenging, even when the deformation signal has a low gradient, such as that due to interseismic displacement. Here, we present a novel algorithm to unwrap low-gradient interferograms more efficiently and reliably using a semantic segmentation neural network. We first partition large-scale interferograms into overlapping patches and employ a trained Segformer network, making full use of spatial f...