An Unsupervised Deep Learning Method for Subsurface Target Detection in Radar Sounder Data

• Published in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
• Main Authors: Donini, Elena, Bovolo, Francesca, Bruzzone, Lorenzo
• Format: Conference Object
• Language: English
• Published: IEEE 2021
• Subjects: deep learning; statistical analysis; subsurface target detection; radar sounder; South Pole; South pole
• Online Access: https://hdl.handle.net/11572/322995; https://doi.org/10.1109/IGARSS47720.2021.9554785; https://ieeexplore.ieee.org/document/9554785/authors#authors

Radar sounder data are widely used for investigating geological structures and processes in the subsurface of icy and arid areas. Visual interpretation is one of the main techniques used in the literature to extract information from radargrams. There exist some automatic approaches but mostly supervised. However, no methods exploit deep learning in an unsupervised way. Here, we propose an automatic and unsupervised technique for extracting information on the subsurface geological targets. The technique is built upon three steps: i) generation of a coarse segmentation map based on the radargram statistical properties, ii) refinement of the coarse map with deep learning to detect target reflections, and iii) analysis of the deep features to identify buried targets. We tested the proposed method on MARSIS radar data acquired near the South Pole of Mars. The experimental results prove the effectiveness of the proposed method.