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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Super-Resolution of Synthetic Aperture Radar Complex Data by Deep-Learning


    Beteiligte:


    Erscheinungsdatum :

    27.06.2022


    Format / Umfang :

    2282681 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Lynx: a high-resolution synthetic aperture radar

    Tsunoda, S.I. / Pace, F. / Stence, J. et al. | IEEE | 2000


    Lynx: a high-resolution synthetic aperture radar

    Tsunoda, Stanley I. / Pace, Frank / Stence, Jesse et al. | SPIE | 1999


    Lynx: A High- Resolution Synthetic Aperture Radar

    Tsunoda, S. I. / Pace, F. / Stence, J. et al. | British Library Conference Proceedings | 2000


    SEASAT Synthetic Aperture Radar Data

    Henderson, F. M. | NTRS | 1981


    Synthetic aperture radar

    Tema Archiv | 1976