End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery

Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliab...

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Bibliographic Details
Published in:Applied Sciences
Main Authors: Yiheng Cai, Shaobin Hu, Shinan Lang, Yajun Guo, Jiaqi Liu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
end-to-end network
classification
ice sheet subsurface targets
radar imagery
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Greenland
Pyramid
Antarc*
Antarctica
Ice Sheet
Online Access:https://doi.org/10.3390/app10072501
https://doaj.org/article/3e7032a2bc684da782db5e4552f11979
Description
Summary:Sea level rise, caused by the accelerated melting of glaciers in Greenland and Antarctica in recent decades, has become a major concern in the scientific, environmental, and political arenas. A comprehensive study of the properties of the ice subsurface targets is particularly important for a reliable analysis of their future evolution. Newer deep learning techniques greatly outperform the traditional techniques based on hand-crafted feature engineering. Therefore, we propose an efficient end-to-end network for the automatic classification of ice sheet subsurface targets in radar imagery. Our network uses bilateral filtering to reduce noise and consists of ResNet module, improved Atrous Spatial Pyramid Pooling (ASPP) module, and decoder module. With radar images provided by the Center of Remote Sensing of Ice Sheets (CReSIS) from 2009 to 2011 as our training and testing data, experimental results confirm the robustness and effectiveness of the proposed network in radargram.

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