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

• 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

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.