Deep learning based automatic grounding line delineation in DInSAR interferograms - Ramanath Tarekere, Sindhu; Krieger, Lukas; Heidler, Konrad; Floricioiu, Dana (2023)
The grounding line is a subsurface geophysical feature that divides the grounded ice sheet and floating ice shelf. Knowledge of its precise location is required for estimating ice sheet mass balance, as ice discharged from the interior is typically calculated at the grounding line [1], [2]. While grounding lines in Greenland have only a minimal extension, in Antarctica, they span about 75% of its coastline. The bending of ice shelves due to ocean tides causes them to migrate several kilometers over a tidal cycle within a transition region called the grounding zone. This short-term displacement adds to the difficulty in grounding line detection on a featureless ice surface. Nevertheless, various remote sensing methods can currently detect grounding lines on a continental scale. In particular, Differential Interferometric Synthetic Aperture Radar (DInSAR) is used to measure the deformation which occurs at the grounding line due to tidal flexure of ice shelves with sub-centimeter accuracy [3]. If coherence is preserved between the SAR repeat passes, the vertical ice deformation at the grounding zone is visible in the double difference interferogram as a dense fringe belt. The landward-most fringe is considered a good approximation of the actual grounding line. Although the generation of DInSAR interferograms is already automatized, the identification of the landward-most fringe and its digitization is still majorly performed manually by human operators. Besides being labour and time-intensive, manual delineations are inconsistent due to varying interpretations of experts in identifying the landward fringe, especially in areas with poor coherence or intricate fringe patterns. In the present study, we attempt to automate the delineation by employing a Convolutional Neural Network (CNN). We developed an automatic workflow that handles the preparation of the training feature stack, training and inference of the neural network and the post-processing of network-generated delineations. The CNN architecture is based on ...