# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 1 Introduction  
In recent years, global warming has exacerbated the melting of glaciers in Greenland and Antarctica, which has considerable influence on sea level rise and, subsequently, the safety of those living in coastal and seaside locations. In order to gather data about glacier melting, it is vital to identify and quantify changes in the ice and bedrock layers of these glaciers. Historically, glaciologists probed the subsurface structure of ice sheets in polar regions by drilling ice cores, but newer methods--such as ground-penetrating radar (GPR) technology--enable scientists to gather robust data sets quickly and efficiently. Prior work on this topic involves analyzing data from radar sounder instruments (known as radargrams or echograms) to draw inferences about the properties of the ice sampled. Radar sounders, which are usually operated on airborne or satellite platforms, are active instruments that can perform non-intrusive depth measurements of the subsurface structure of the ice sheets on a large spatial scale. While the advent of ground-penetrating radar made the data collection process more efficient, the data analysis process is still intensely time-consuming because it is typically done by hand. Newer work in this field utilizes image processing, computer vision, and deep learning techniques to automatically or semi-automatically determine ice surface and bottom boundaries from echograms [1; 2; 3; 4; 5; 6; 7]. [PERSON] et al. [1] employs both the edge-based and active contour methodologies to automate the task of locating polar ice and bedrock layers from airborne radar data acquired over Greenland and Antarctica. In the edge-based approach, edge detection, thresholding, and edge following are utilized to identify the layers of interest for the ice thickness estimation. The active contour approach involves fitting a contour to the boundary using image and contour costs, as well as a gradient force that pushes the contour upward from the bottom of the image. Both methods have theirpros and cons; the edge-based method is more efficient, but the active contour method produces more robust results [1]. A third technique, the level-set model, is better at identifying curved boundaries by embedding the evolving curve into a higher dimensional surface. [PERSON] et al. [2] uses a level-set technique for estimating bedrock and surface layers, but find it problematic because of the need to reinitialize the curve manually for each radar image. Therefore, [PERSON] et al. [3] introduces the distance regularization term, which essentially maintains the regularity of the level-set and leads to a stable numerical procedure without the need for reinitialization. Recently, [PERSON] et al. [4] proposes a novel approach that automatically detects the complex topology of the ice surface and bottom boundaries based on the charged particle concept. This method detects the contours in the image based on Coulomb's Law and the assumption that each pixel is an electrically charged particle. Another approach is to use graphical models to detect ice layers in radar echograms. [PERSON] et al. [5] frame boundary tracking as an inference problem and employs a Markov Chain Monte Carlo (MCMC) technique to sample from the joint distribution over all possible layers that exist on a given image. [PERSON] et al. [6] revisits this issue and uses a tree-reweighted message passing (TRW) technique, which first generates a seed surface subject with a set of constraints, and then incorporates additional sources of evidence to refine it via discrete energy minimization. [PERSON] et al. [7] further improves upon the method of [PERSON] et al. [6] by incorporating additional domain-specific knowledge into the cost functions and modeling algorithms. There are also some efforts to achieve the automatic classification of ice subsurface targets in echograms [8; 9; 10; 11]. [PERSON] et al.