Radar sounders (RSs) are widely used to image profiles (radargrams) of the subsurface of planetary bodies and the Earth. However, despite the huge scientific return from radargram analyses, their horizontal and vertical resolutions are limited by technical factors. Even if methods exist for improving the resolution, these are still limited by technical factors and introduce artifacts. This paper proposes an unsupervised deep-learning method that synthesizes accurate super-resolved radargrams overcoming these limitations. The method adopts the Cycle-Consistent Adversarial Network (CyleGAN) that learns the mapping function between the low- and high-resolution data distributions. The network is adapted to match the low- and high-resolution radargram characteristics, including the differences in dimensions and radiometric properties. The proposed method was successfully validated on airborne data at higher resolution and simulated data with lower resolution.

An Unsupervised Deep Learning Method for the Super-Resolution of Radar Sounder Data / Donini, Elena; Kasibovic, Amar; Garcia, Miguel Hoyo; Bruzzone, Lorenzo; Bovolo, Francesca. - ELETTRONICO. - (2022), pp. 1696-1699. ((Intervento presentato al convegno IEEE International Geoscience and Remote Sensing Symposium tenutosi a Kuala Lumpur, Malaysia nel 17-22 July 2022 [10.1109/IGARSS46834.2022.9884343].

An Unsupervised Deep Learning Method for the Super-Resolution of Radar Sounder Data

Donini, Elena;Garcia, Miguel Hoyo;Bruzzone, Lorenzo;Bovolo, Francesca
2022-01-01

Abstract

Radar sounders (RSs) are widely used to image profiles (radargrams) of the subsurface of planetary bodies and the Earth. However, despite the huge scientific return from radargram analyses, their horizontal and vertical resolutions are limited by technical factors. Even if methods exist for improving the resolution, these are still limited by technical factors and introduce artifacts. This paper proposes an unsupervised deep-learning method that synthesizes accurate super-resolved radargrams overcoming these limitations. The method adopts the Cycle-Consistent Adversarial Network (CyleGAN) that learns the mapping function between the low- and high-resolution data distributions. The network is adapted to match the low- and high-resolution radargram characteristics, including the differences in dimensions and radiometric properties. The proposed method was successfully validated on airborne data at higher resolution and simulated data with lower resolution.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
New York
IEEE
978-1-6654-2792-0
Donini, Elena; Kasibovic, Amar; Garcia, Miguel Hoyo; Bruzzone, Lorenzo; Bovolo, Francesca
An Unsupervised Deep Learning Method for the Super-Resolution of Radar Sounder Data / Donini, Elena; Kasibovic, Amar; Garcia, Miguel Hoyo; Bruzzone, Lorenzo; Bovolo, Francesca. - ELETTRONICO. - (2022), pp. 1696-1699. ((Intervento presentato al convegno IEEE International Geoscience and Remote Sensing Symposium tenutosi a Kuala Lumpur, Malaysia nel 17-22 July 2022 [10.1109/IGARSS46834.2022.9884343].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/354893
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