Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning.

ABSTRACT: Delineating the grounding line of marine-terminating glaciers-where ice starts to become afloat in ocean waters-is crucial for measuring and understanding ice sheet mass balance, glacier dynamics, and their contributions to sea level rise. This task has been previously done using time-consuming, mostly-manual digitizations of differential interferometric synthetic-aperture radar interferograms by human experts. This approach is no longer viable with a fast-growing set of satellite observations and the need to establish time series over entire continents with quantified uncertainties. We present a fully-convolutional neural network with parallel atrous convolutional layers and asymmetric encoder/decoder components that automatically delineates grounding lines at a large scale, efficiently,

SUBMITTER: Mohajerani Y 

PROVIDER: S-EPMC7925556 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature