This time we invited Giampaolo Ferraioli, Associate Professor with Università degli Studi di Napoli Parthenope, Italy, to share with us his experience with non-Polar-related SAR and machine learning applications.

Synthetic Aperture Radar (SAR) imaging is a powerful remote sensing technique for a wide range of applications, including environmental monitoring, disaster management, and military surveillance.

In recent years, deep learning has emerged as a paradigm for SAR image analysis. Leveraging the capabilities of artificial neural networks, deep learning algorithms have demonstrated remarkable success in various SAR applications. However, most existing deep learning methods for SAR image processing focus solely on data-driven approaches, neglecting the physical characteristics of SAR systems.

In this seminar the attention will be focused on the added value of physical based approaches.

The integration of physical-based approaches with deep learning in SAR image processing opens new avenues for advanced analysis and interpretation of SAR data. The synergy between the strengths of physical modeling and the learning capabilities of deep neural enables more accurate and insightful information extraction from SAR images.

Please join our seminar in person at the CIRFA headquarters in Forskiningsparken or online (Teams) on Wednesday 14 June at 14:00. This deviates by a day from our usual seminar time as Giampaolo will be one of the opponents on the PhD defense of our Eduard Khachatrian, taking most of the Thursday!