Regression Networks for Calculating Englacial Layer Thickness

Ice thickness estimation is an important aspect of ice sheet modelling. In this work, we use convolutional neural networks (CNN) with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images captured over northwest Greenland. We experiment with some state-of-the-art CNNs to obtain a mean absolute error of 1.251 pixels of thickness estimation over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.