# End-to-End Classification Network for Ice Sheet Subsurface Targets in Radar Imagery
## 1 Introduction
[8; 9] exploits the statistical properties of the radar signal to pre-process the radar image data in order to generate a statistical map of the subsurface, and then deploys a segmentation algorithm that had been attuned to that specific study area. [PERSON] et al. [10; 11] develops an automatic classification system for subsurface targets, which includes extraction of relevant features based on both the statistical properties of the radar sounder signals and the spatial distribution of the ice subsurface targets. Once specific features are identified, they are then categorized with a support vector machine (SVM) classifier. The classification of each category can be used to focus on the study of specific areas. The classification of the whole bedrock area can be used for geological studies, to understand the thickness of bedrock, to evaluate the type of bedrock material and to derive the absorption characteristics of bedrock [12]. Meanwhile, the thickness of the ice column can be calculated by the classification of layers and bedrock. At present, the Center of Remote Sensing of Ice Sheets (CReSIS) provide a wide variety of radar data from various radar equipments. A radargram provided by CReSIS) is a 2-D matrix with nR rows (samples i) and nC columns (frames j). The typical radargram model includes free space, layers, echo-free zone (EFZ), bedrock, and noise, as shown in Figure 1 (water and freeze-on ice are not considered here). Layers and bedrock are two physical components of the ice sheet subsurface. The ice column consists of a sequence of ice layers. Bedrock is the deepest reflection area of radar wave, and completely attenuates the radar wave. Therefore, the radar equipment under the bedrock can only receive noise. Especially EFZ, which is not a physical component, is the result of the lack of coherent reflector due to the layer disturbance caused by the ice flow at the base interface [13]. In the EFZ, the reflected wave is buried in the thermal noise, so it has a similar distribution with the noise [10]. For this reason, in the automatic classification of ice subsurface targets, the EFZ and noise classes are merged within a single no backscattering target class [10]. Therefore, free space, layers, bedrock, and noise (including EFZ region) are regarded as the classification targets in this paper. The traditional classification methods need to design features manually, which are high complexity and slow speed, and are not suitable for large, complex datasets. In a more recent development, deep learning technology is employed to better estimate the ice and bedrock boundaries in glacier echograms. [PERSON] et al. [14] combines holistically-nested edge detection (HED) [15] with the undecimated wavelet transform technique to develop an end-to-end ice boundary detection network. [PERSON] et al. [16] proposes a multi-task spatiotemporal neural network that combines 3D ConvNets and recurrent neural network (RNN) to estimate ice surface boundaries from sequences of tomographic radar images. However, the identification of the ice subsurface targets using deep learning technology has not been fully explored. Deep learning algorithms are efficient in many public data sets (e.g., Cityscapes dateset, PASCAL-VOC dataset) because they can automatically learn the features of the different data scales without manual intervention. [PERSON] et al. [17; 18; 19] propose a series of DeepLab networks for image pixel classification, where the latest network, DeepLabv3+, provides the accurate and high resolution results in PASCAL-VOC2012. [PERSON] et al. [20] introduces an object context pooling (OCP) scheme and focus on the context aggregation strategy for robust classification. [PERSON] et al. [21] propose a dual attention network (DANet) to adaptively integrate local features with their global dependencies. Therefore, it is worth exploring strategies for applying these deep learning methods to the classification of the ice subsurface targets.