Novel methods for information extraction and geological product generation from radar sounder data

This Ph.D. thesis presents advancements in the analysis of radar sounder data. Radar sounders (RSs) are remote sensors that transmit an electromagnetic (EM) wave at the nadir direction that penetrates the subsurface. The backscattered echoes captured by the RS antenna are coherently summed to generate an image of the subsurface profile known as a radargram. The first focus of this work is to automate the segmentation of radargrams using deep learning methodologies while minimizing the need for labeled training data. The surge in radar sounding data volume necessitates efficient automated methods. However, the amount of training labeled data in this field is strongly limited. This first work introduces a transfer learning framework based on deep learning tailored for radar sounder data that minimizes the training data requirements. This method automatically identifies and segments geological units within radargrams acquired in the cryosphere. With the cryosphere being a critical indicator of climate change, understanding its dynamics is paramount. Geological details within radargrams, such as the basal interface or the inland and floating ice, are key to this understanding. Our work shifts the focus to uncharted territory: the coastal areas of Antarctica. Novel targets such as floating ice and crevasses add complexity to the data, but the transfer learning framework minimizes the need for extensive labeled training data. The results, based on data from Antarctica, confirm the effectiveness of the approach, promising adaptability to other targets and radar data from existing and future planetary missions like RIME and SRS. The second focus of this thesis explores the generation of novel and improved geological data products by harnessing the unique characteristics of radar sounder data, including subsurface information and so-called “unwanted” clutter. The thesis introduces two methods that use RS data to generate geological products. The first contribution proposes a global high-frequency radar image of Mars. This product delivers a novel, comprehensive global radar image of Mars, capturing both surface and shallow subsurface structures. The method unlocks the potential to explore concealed Martian geology and further understand Martian geological features like dust, revealing possible candidate large dust deposits that were unknown until now. Furthermore, this method can potentially offer insights into celestial bodies beyond Mars, such as the detection of new lunar facets and Venusian geological formations. The third contribution aims to generate Digital Elevation Models (DEM) from single swath radargrams. The activity addresses the challenge of precise bed DEM estimations in Antarctica. Bed topography is critical in ice modeling and mass balance calculations, yet existing methods face limitations. To overcome these, we employ a generative adversarial network (GAN) approach that utilizes clutter information from single radargrams. This innovative technique promises to refine bed DEMs and enhance our understanding of glacier erosion and ice dynamics. The proposed methodologies were validated with data acquired on both Earth and Mars, showing promising results and confirming their effectiveness.