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.
|Name||IEEE International Workshop on Metrology for Aerospace (MetroAeroSpace)|
|Conference||2022 IEEE 9th International Workshop on Metrology for AeroSpace (MetroAeroSpace)|
|Abbreviated title||MetroAeroSpace 2022|
|Period||27/06/22 → 29/06/22|
- expectation maximization
- polarimetric radar
- synthetic aperture radar