Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms
This work addresses the challenge of accurately tracking snow layers in polar ice caps to support sea-level rise projections, representing an incremental improvement in domain-specific deep learning for environmental monitoring.
The paper tackles the problem of automatically detecting snow layers in radar echograms to estimate thicknesses for sea-level rise studies, achieving a mean absolute error of 3.31 pixels and 94.3% average precision with improved generalizability over state-of-the-art methods.
Airborne radar sensors capture the profile of snow layers present on top of an ice sheet. Accurate tracking of these layers is essential to calculate their thicknesses, 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 snow layers is a challenging problem. In our work, we develop wavelet-based multi-scale deep learning architectures for these radar echograms to improve snow layer detection. These architectures estimate the layer depths with a mean absolute error of 3.31 pixels and 94.3% average precision, achieving higher generalizability as compared to state-of-the-art snow layer detection networks. These depth estimates also agree well with physically drilled stake measurements. Such robust architectures can be used on echograms from future missions to efficiently trace snow layers, estimate their individual thicknesses and thus support sea-level rise projection models.