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 and Yan, Linjie (2023) Super-resolution of synthetic aperture radar complex data by deep-learning. IEEE Access, 11. pp. 23647-23658. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2023.3251565)

[thumbnail of Addabbo-etal-IEEEA-2023-Super-resolution-of-synthetic-aperture-radar-complex-data-by-deep-learning]
Preview
 Text. Filename: Addabbo_etal_IEEEA_2023_Super_resolution_of_synthetic_aperture_radar_complex_data_by_deep_learning.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (5MB)| 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 spatial resolution of low-resolution SAR images while retaining the complex image accuracy. Results on simulated 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, Orlando, Danilo and Yan, Linjie;
CORE (COnnecting REpositories)