  Fluctuations of the glacier calving front have an important influence over
the ice flow of whole glacier systems. It is therefore important to precisely
monitor the position of the calving front. However, the manual delineation of
SAR images is a difficult, laborious and subjective task. Convolutional neural
networks have previously shown promising results in automating the glacier
segmentation in SAR images, making them desirable for further exploration of
their possibilities. In this work, we propose to compute uncertainty and use it
in an Uncertainty Optimization regime as a novel two-stage process. By using
dropout as a random sampling layer in a U-Net architecture, we create a
probabilistic Bayesian Neural Network. With several forward passes, we create a
sampling distribution, which can estimate the model uncertainty for each pixel
in the segmentation mask. The additional uncertainty map information can serve
as a guideline for the experts in the manual annotation of the data.
Furthermore, feeding the uncertainty map to the network leads to 95.24% Dice
similarity, which is an overall improvement in the segmentation performance
compared to the state-of-the-art deterministic U-Net-based glacier segmentation
pipelines.
