Snow Radar Layer Tracking Using Iterative Neural Network Approach

This paper presents preliminary results using a fully connected neural network (NN) to automatically track the internal layers of snow radar echograms using an iterative "row-block-column" approach. Snow radar images, when accurately tracked, provide relevant information for estimating snow accumulation rates in polar regions which is a key measurement needed to understand and predict the impact of climate warming in Greenland and Antarctica. A multiclass NN was designed and trained with a training set of 121,408 columns of simulated snow radar data and learns to automatically track the internal layers with an accuracy of 92.8%, a RMSE of 0.24 pixels, and with 98% of pixel errors less than or equal to 1 pixel.