Super-resolution of synthetic aperture radar complex data by deep-learning

Addabbo, Pia and Bernardi, Mario Luca and Biondi, Filippo and Cimitile, Marta and Clemente, Carmine and Fiscante, Nicomino and Giunta, Gaetano and Orlando, Danilo; (2022) Super-resolution of synthetic aperture radar complex data by deep-learning. In: 2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace). IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace) . IEEE, ITA, pp. 237-241. ISBN 9781665410762 (https://doi.org/10.1109/metroaerospace54187.2022.9...)

[thumbnail of Addabbo-etal-MetroAeroSpace-2022-Super-resolution-of-synthetic-aperture-radar-complex-data-by-deep-learning]
Preview
 Text. Filename: Addabbo_etal_MetroAeroSpace_2022_Super_resolution_of_synthetic_aperture_radar_complex_data_by_deep_learning.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (1MB)| Preview

Abstract

One of the greatest limitations of Synthetic Aperture Radar imagery is the capability to obtain an arbitrarily high spatial resolution. Indeed, despite optical sensors, this capability is not just limited by the sensor technology. Instead, improving the SAR spatial resolution requires large transmitted bandwidth and relatively long synthetic apertures that for regulatory and practical reasons are impossible to be met. This issue gets particularly relevant when dealing with Stripmap mode acquisitions and with relatively low carrier frequency sensors (where relatively large bandwidth signals are more difficult to be transmitted). To overcome this limitation, in this paper a deep learning based framework is proposed to enhance the SAR image spatial resolution while retaining the complex image accuracy. Results on simuated and real SAR data demonstrate the effectiveness of the proposed framework.

ORCID iDs

Addabbo, Pia, Bernardi, Mario Luca, Biondi, Filippo, Cimitile, Marta, Clemente, Carmine ORCID logoORCID: https://orcid.org/0000-0002-6665-693X, Fiscante, Nicomino, Giunta, Gaetano and Orlando, Danilo;
  • Item type:Book Section
    ID code:82242
    Dates:
    Date
    Event
    18 August 2022
    Published
    27 June 2022
    Published Online
    27 March 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:07 Sep 2022 10:32
    Last modified:11 Nov 2024 15:30
    URI:https://strathprints.strath.ac.uk/id/eprint/82242
CORE (COnnecting REpositories)