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.