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

Pia Addabbo, Mario Luca Bernardi, Filippo Biondi, Marta Cimitile, Carmine Clemente, Nicomino Fiscante, Gaetano Giunta, Danilo Orlando, Linjie Yan

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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.
Original languageEnglish
Pages (from-to)23647-23658
Number of pages12
JournalIEEE Access
Early online date2 Mar 2023
Publication statusPublished - 13 Mar 2023


  • SAR
  • super-resolution
  • deep learning
  • CNN
  • cosmo-skymed
  • Synthetic Aperture Radar
  • Convolutional Neural Network


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