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.

An Unsupervised Deep Learning Method for Subsurface Target Detection in Radar Sounder Data / Donini, Elena; Bovolo, Francesca; Bruzzone, Lorenzo. - ELETTRONICO. - (2021), pp. 2955-2958. (Intervento presentato al convegno IGARSS 2021 tenutosi a Brussels, Belgium nel 12-16 July 2021) [10.1109/IGARSS47720.2021.9554785].

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

Donini, Elena;Bovolo, Francesca;Bruzzone, Lorenzo
2021-01-01

Abstract

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.
2021
IEEE 2021 Int. Geoscience and Remote Sensing Symposium
New York, Stati Uniti
IEEE
978-1-6654-0369-6
Donini, Elena; Bovolo, Francesca; Bruzzone, Lorenzo
An Unsupervised Deep Learning Method for Subsurface Target Detection in Radar Sounder Data / Donini, Elena; Bovolo, Francesca; Bruzzone, Lorenzo. - ELETTRONICO. - (2021), pp. 2955-2958. (Intervento presentato al convegno IGARSS 2021 tenutosi a Brussels, Belgium nel 12-16 July 2021) [10.1109/IGARSS47720.2021.9554785].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/322995
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