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-:(2022), pp. 1696-1699. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 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.File | Dimensione | Formato | |
---|---|---|---|
An_Unsupervised_Deep_Learning_Method_for_the_Super-Resolution_of_Radar_Sounder_Data.pdf
Solo gestori archivio
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
6.8 MB
Formato
Adobe PDF
|
6.8 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione