IEEE IGARSS 2022 || Kuala Lumpur, Malaysia || 17

MO4.O2.4

A PHYSICS-BASED ML APPROACH FOR CORN PLANT HEIGHT ESTIMATION WITH SIMULATED SAR DATA

Lorenzo Giuliano Papale, Fabio Del Frate, Leila Guerriero, Giovanni Schiavon, Tor Vergata University of Rome, Italy

Session:

Quantum computing and ML in GRSS Oral In-Person

Location:

Conference Hall 2

Presentation Time:

Mon, 18 Jul, 17:00 - 17:15 Malaysia Time (UTC +8)
Mon, 18 Jul, 11:00 - 11:15 Central European Time (UTC +1)
Mon, 18 Jul, 09:00 - 09:15 UTC
Mon, 18 Jul, 05:00 - 05:15 Eastern Time (UTC -5)

Session Co-Chairs:

Gabriele Cavallaro, Forschungszentrum Jülich and Mihai Datcu, German Aerospace Center (DLR)

Session Manager:

Nik Zainun Nik Mood

Resources

No resources available.

Session MO4.O2

MO4.O2.1: HYBRID QUANTUM-CLASSICAL WORKFLOWS IN MODULAR SUPERCOMPUTING ARCHITECTURES WITH THE JÜLICH UNIFIED INFRASTRUCTURE FOR QUANTUM COMPUTING

Gabriele Cavallaro, Thomas Lippert, Kristel Michielsen, Forschungszentrum Jülich, Germany; Morris Riedel, University of Iceland, Iceland

MO4.O2.2: HOW QUANTUM COMPUTING-FRIENDLY MULTISPECTRAL DATA CAN BE?

Manish Kumar Gupta, Piotr Gawron, Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Poland; Martin Beseda, Technical University of Ostrava, Czechia

MO4.O2.3: Quantum Computing for Earth Observation Scientists – Introducing some key concepts

Tobias Guggemos, Andreas Spoerl, German Aearospace Center (DLR), Germany

MO4.O2.4: A PHYSICS-BASED ML APPROACH FOR CORN PLANT HEIGHT ESTIMATION WITH SIMULATED SAR DATA

Lorenzo Giuliano Papale, Fabio Del Frate, Leila Guerriero, Giovanni Schiavon, Tor Vergata University of Rome, Italy

MO4.O2.5: RETRIEVAL OF RAINFALL INFORMATION BY SPACEBORNE C-BAND SAR BASED ON MACHINE LEARNING

Shanshan Mu, Xiaofeng Li, Institute of Oceanology, Chinese Academy of Sciences, China