  The vast majority of the outlet glaciers and ice streams of the polar ice
sheets end in the ocean. Ice mass loss via calving of the glaciers into the
ocean has increased over the last few decades. Information on the temporal
variability of the calving front position provides fundamental information on
the state of the glacier and ice stream, which can be exploited as calibration
and validation data to enhance ice dynamics modeling. To identify the calving
front position automatically, deep neural network-based semantic segmentation
pipelines can be used to delineate the acquired SAR imagery. However, the
extreme class imbalance is highly challenging for the accurate calving front
segmentation in these images. Therefore, we propose the use of the Mathews
correlation coefficient (MCC) as an early stopping criterion because of its
symmetrical properties and its invariance towards class imbalance. Moreover, we
propose an improvement to the distance map-based binary cross-entropy (BCE)
loss function. The distance map adds context to the loss function about the
important regions for segmentation and helps accounting for the imbalanced
data. Using Mathews correlation coefficient as early stopping demonstrates an
average 15% dice coefficient improvement compared to the commonly used BCE. The
modified distance map loss further improves the segmentation performance by
another 2%. These results are encouraging as they support the effectiveness of
the proposed methods for segmentation problems suffering from extreme class
imbalances.
