Deep neural network 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 a 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]. Grounding lines move back and forth as ice shelves bend and flex due to ocean tides. Identifying their migration patterns can provide insights into understanding ice sheet dynamics and overall ice sheet stability [2] and thereby improve the accuracy of numerical ice sheet models. The spatial and temporal resolution of past and current satellite missions has enabled regular, continent-wide observation of Antarctica and other isolated glaciers with floating ice tongues. In particular, the high sensitivity of Interferometric SAR measurements to ground deformation has resulted in its application to grounding line location (GLL) mapping [3]. Specifically, the deformation at the grounding zone resulting from tidal flexure of the ice shelf is isolated from ice motion and topography in Differential InSAR (DInSAR) interferograms, under the assumption of steady ice velocity within the chosen temporal baseline. The tidal deformation is visible as a dense fringe belt and its landward limit is manually digitised as the GLL. Apart from being labour and time intensive, manual delineations are also inconsistent due to varying interpretations of experts in identifying the landward fringe, especially in areas with poor coherence or intricate fringe patterns. The concept of automatic GLL delineation has recently gained attention and seen the development of several methodologies. [4] demonstrated a semi-automatic method that estimates the fringe frequency of wrapped phase in DInSAR interferograms. The grounding zone can be directly identified by computing the gradient of the estimated frequencies, thereby avoiding phase unwrapping. However, this approach requires an a priori grounding zone location. [5] developed a deep learning based automatic ...