Exhaustive investigations of the ice sheet subsurface can be carried out by analyzing the information contained in the huge archives of radargrams acquired by dedicated radar sounder (RS) instruments. The analysis can be done by using properly designed automatic techniques for a quantitative, objective, and reliable extraction of information from radargrams. Unfortunately, the definition and development of such automatic techniques have only been marginally addressed in the literature. In this paper, we propose a novel and efficient system for the automatic classification of ice subsurface targets present in radargrams. The core of the system is represented by the extraction of a set of features for target discrimination. The features are based on both the specific statistical properties of the RS signal and the spatial distribution of the ice subsurface targets. Such features are then provided as input to an automatic classifier based on support vector machine. Experimental results obtained on two real-world data sets acquired by airborne-mounted RSs in large regions of Antarctica confirm the robustness and effectiveness of the proposed classification system.

This paper presents the Radar for Icy Moon Exploration (RIME) that is a fundamental payload in the Jupiter Icy Moon Explorer (JUICE) mission of the European Space Agency (ESA). RIME is a radar sounder aimed to study the subsurface of Jupiter's icy moons Ganymede, Europa and Callisto. The paper illustrates the main goals of RIME, its architecture and parameters and some recent advances in its design

The rise in the temperature of the planet has a very negative impact on the subsurface dynamics of the Earth Polar Regions. This makes it crucial to investigate the features present in the ice subsurface. The analysis of these features is typically performed manually, by examining radargrams acquired by radar sounder (RS) instruments operated at the Polar Caps. However, in order to cope with the very large amount of data that RSs can acquire, it is necessary to develop data analysis techniques that can identify and extract subsurface features automatically. To address this problem, in this paper we propose a novel technique for the automatic estimation of the ice thickness and bedrock properties from RS data acquired in Antarctica. The proposed technique generates a statistical map of the subsurface by exploiting the statistical properties of the radar signal. Then, it applies a segmentation algorithm properly tuned to the characteristics of the investigated areas. In order to assess the effectiveness of the proposed technique, we analyzed its performance when applied to Multichannel Coherent Radar Depth Sounder (MCoRDS) data acquired in Antarctica.

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