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

• Main Author: Hoyo Garcia, Miguel
• Other Authors: Bovolo, Francesca, Bruzzone, Lorenzo
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Università degli studi di Trento 2024
• Subjects: radar sounder; planetary exploration; cryosphere; deep learning; subsurface; Antarc*; Antarctica
• Online Access: https://hdl.handle.net/11572/405491; https://doi.org/10.15168/11572_405491

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. ...