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.