A Deep Learning Architecture for Semantic Segmentation of Radar Sounder Data

During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to...

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Bibliographic Details
Published in:IEEE Transactions on Geoscience and Remote Sensing
Main Authors: Donini, Elena, Bovolo, Francesca, Bruzzone, Lorenzo
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Basal boundary conditions
basal units
convolutional neural network
cryosphere
deep learning
radar sounder (RS)
remote sensing
Greenland
Pyramid
West Antarctica
Antarc*
Antarctica
Ice Sheet
North Greenland
Online Access:http://hdl.handle.net/11572/331172
https://doi.org/10.1109/TGRS.2021.3125773
https://ieeexplore.ieee.org/document/9605569
Description
Summary:During the last decades, radar sounders provided direct measurements (radargrams) of the Earth’s polar caps’ subsurface. Radargrams are of critical importance for a better understanding of glaciologic structures and processes of the ice sheet in the framework of climate change. This article aims to automatically extract information on basal boundary conditions given their substantial relevance for modeling the ice-sheet processes, such as the sliding. We introduce a novel automatic method based on deep learning to detect the basal layer and basal units in radargrams acquired in the inland of icy areas. Radargrams are segmented into englacial layers, bedrock, basal units, and noise-limited regions; the latter includes the echo-free zone (EFZ), thermal noise, and signal perturbation. The network is a U-Net with attention gates and the Atrous Spatial Pyramid Pooling (ASPP) module that automatically extract semantically meaningful features at different scales. Experimental results on two datasets acquired in north Greenland and west Antarctica by the Multichannel Coherent Radar Depth Sounder (MCoRDS3) indicate a high overall segmentation accuracy. The accuracy of basal ice and signal perturbation detection is high, and that of the other classes is comparable with the literature techniques based on handcrafted features. The results show the effectiveness of the proposed method in automatically extracting semantically meaningful features to segment radargrams and map the basal layer and basal units.

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