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

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, and accompanied by uncertainty estimates. Our procedure detects grounding lines within 232 m in 100-m posting interferograms, which is comparable to the performance achieved by human experts. We also f...划定入海冰川（marine-terminating glaciers）的接地线（grounding line）——即冰层开始漂浮于海水之中的位置——对于测量与理解冰盖质量平衡、冰川动力学及其对海平面上升的贡献至关重要。此前该任务需依靠人类专家对差分干涉合成孔径雷达干涉图（differential interferometric synthetic-aperture radar interferograms）开展耗时且以手动数字化为主的操作。随着卫星观测数据量快速增长，且需要在整个大陆范围内建立带有量化不确定性的时间序列，该方法已不再适用。本文提出一种搭载并行空洞卷积层与非对称编码器-解码器组件的全卷积神经网络（fully-convolutional neural network），可高效、大规模地自动划定接地线，并附带不确定性估计结果。在采样间距为100米的干涉图中，我们的方法对接地线的检测误差在232米以内，性能可与人类专家媲美。此外我们还...