Abstract

The World Climate Research Programme (WCRP) has recently reconfirmed the importance of a better understanding of the Cryosphere for advancing the analysis, modeling and prediction of climate change and its impact on the environment and society. One of the most complete collection of information about the ice sheets and glaciated areas is contained in the data (radargrams) acquired by Radar Sounder (RS) instruments. The need to better understand the structure of the ice sheets and the availability of enourmous quantities of radargrams call for the development of automatic techniques for an efficient extraction of information from RS data. This topic has been only marginally addressed in the literature. Thus, in this thesis we address this challenge by contributing with four novel automatic techniques for the analysis of radargrams acquired at the ice sheets. The first contribution of this thesis presents a system for the automatic classification of ice subsurface targets in RS data. 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. The second contribution is an unsupervised model-based technique for the automatic detection and property estimation of ice subsurface targets. This is done by using the parameters of the RS system combined with the output of an automatic image segmentation algorithm. The third contribution presents an automatic technique for the local 3D reconstruction of the ice sheet. It is based on the use of RS and altimeter (ALT) data, and relies on the use of a geostatistical interpolation method and on several statistical measures for validating the interpolation results and the quality of interpolation. The fourth contribution presents a technique for the automatic estimation of radar power losses in ice as a continuous non-linear function of depth, by using RS and ice core data. The technique relies on the detection of ice layers in the RS data, the computation of their reflectivity from the ice core data and the use of the radar equation for loss estimation. Qualitative and quantitative experimental results obtained on real RS data confirm the effectiveness of the first three techniques. Also, preliminary results have been obtained by applying the fourth technique to real RS and ice core data acquired in Greenland. Due to their advantages over the traditional manual approach, e.g., efficiency, objectivity, possibility of jointly analyzing multisensor data (e.g., RS, ALT), the proposed methods can support the scientific community to enhance the data usage for a better modeling and understanding of the ice sheets. Moreover, they will become even more important in the near future, since the volume of data is expected to grow from the increase in airborne and possible Earth Observation spaceborne RS missions.