  Echograms created from airborne radar sensors capture the profile of firn
layers present on top of an ice sheet. Accurate tracking of these layers is
essential to calculate the snow accumulation rates, which are required to
investigate the contribution of polar ice cap melt to sea level rise. However,
automatically processing the radar echograms to detect the underlying firn
layers is a challenging problem. In our work, we develop wavelet-based
multi-scale deep learning architectures for these radar echograms to improve
firn layer detection. We show that wavelet based architectures improve the
optimal dataset scale (ODS) and optimal image scale (OIS) F-scores by 3.99% and
3.7%, respectively, over the non-wavelet architecture. Further, our proposed
Skip-WaveNet architecture generates new wavelets in each iteration, achieves
higher generalizability as compared to state-of-the-art firn layer detection
networks, and estimates layer depths with a mean absolute error of 3.31 pixels
and 94.3% average precision. Such a network can be used by scientists to trace
firn layers, calculate the annual snow accumulation rates, estimate the
resulting surface mass balance of the ice sheet, and help project global sea
level rise.
