Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks

Abstract

Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multi-class semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.

References in corpus (6)

• Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
• Deeply-Supervised Nets
• Richer Convolutional Features for Edge Detection
• Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
• Learning to Teach with Dynamic Loss Functions
• Automatic Estimation of Ice Bottom Surfaces from Radar Imagery

Cited by in corpus (3)

• Regression Networks For Calculating Englacial Layer Thickness
• Automatic Feature Highlighting in Noisy RES Data With CycleGAN
• Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Snow Layers in Radar Echograms