Title:Physical based Deep Learning techniques for SAR image processing 
Speaker:Prof. Giampaolo Ferraioli
Affiliation:Department of Science and Technology, University of Naples, Parthenope

Abstract: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 powerful 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 tutorial 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.

In this talk, several deep learning techniques for SAR image processing will be discussed, with particular focus on SAR despeckling, showing the potential of adding and considering physical models in implementing solutions.

Biograph:

Giampaolo Ferraioli was born in Lagonegro, Italy, in 1982. He received the BS and MS degrees and the Ph.D. degree in Telecommunication Engineering. He has been Visiting Scientist at Département TSI of Télécom ParisTech, Paris, France. Currently, he is an Associate Professor with Università degli Studi di Napoli Parthenope. His main research interests deal with Statistical Signal and Image Processing, Radar Systems, Synthetic Aperture Radar, Image Restoration and Deep Learning. He is author of more than 150 papers published on international journals and on international conference proceedings. He won the “IEEE 2009 Best European PhD Thesis in Remote Sensing” prize, sponsored by IEEE Geoscience and Remote Sensing Society. He serves as Associate Editor of IEEE Geoscience and Remote Sensing Letters, as Associate Editor of IEEE Journal on Miniaturization for Air and Space Systems and he is in the Editorial Board of MDPI Remote Sensing. He is member of the Technical Liaison Committee for IEEE Transactions on Computational Imaging