2020 IEEE International Geoscience and Remote Sensing Symposium

September 26 - October 2, 2020 • Virtual Symposium

2020 IEEE International Geoscience and Remote Sensing Symposium

September 26 - October 2, 2020 • Virtual Symposium

TU2.R20.4

Arbitrary Nonlinear FM Waveform Construction and Ultra-Wideband Synthesis

Samuel Prager, University of Southern California, United States; David Hawkins, NASA Jet Propulsion Laboratory, United States; Mahta Moghaddam, University of Southern California, United States
Session:
Student Paper Contest Finalists II
Track:
Student Paper Competition
Presentation Time:
Tue, 29 Sep, 15:30-15:50 (UTC)
Tue, 29 Sep, 23:30-23:50 China Standard Time (UTC +8)
Tue, 29 Sep, 17:30-17:50 Central Europe Summer Time (UTC +2)
Tue, 29 Sep, 08:30-08:50 Pacific Daylight Time (UTC -7)
Session Co-Chairs:
Joel Johnson, Ohio State University and Francesca Bovolo, Fondazione Bruno Kessler
Session Managers:
Vasala Saicharan and Mrinmoy Dhara

Presentation

Discussion

Resources

Session

TU2.R20.1: MODEL AND DATA UNCERTAINTY FOR SATELLITE TIME SERIES FORECASTING WITH DEEP RECURRENT MODELS
Marc Rußwurm, Technical University of Munich, Germany; Syed Mohsin Ali, Xiao Xiang Zhu, German Aerospace Center, Germany; Yarin Gal, University of Oxford, United Kingdom; Marco Körner, Technical University of Munich, Germany
TU2.R20.2: WIND VECTOR AND WAVE HEIGHT RETRIEVAL IN INLAND WATERS USING CYGNSS
Eric Loria, Andrew O'Brien, The Ohio State University, United States; Valery Zavorotny, CIRES, University of Colorado-Boulder, United States; Cinzia Zuffada, NASA Jet Propulsion Laboratory, California Institute of Technology, United States
TU2.R20.3: STRONG POTENTIAL FOR THE DETECTION OF REFROZEN ICE LAYERS IN GREENLAND’S FIRN BY AIRBORNE RADAR SOUNDING
Riley Culberg, Dustin M. Schroeder, Stanford University, United States
TU2.R20.4: Arbitrary Nonlinear FM Waveform Construction and Ultra-Wideband Synthesis
Samuel Prager, University of Southern California, United States; David Hawkins, NASA Jet Propulsion Laboratory, United States; Mahta Moghaddam, University of Southern California, United States
TU2.R20.5: META-LEARNING FOR FEW-SHOT TIME SERIES CLASSIFICATION
Sherrie Wang, Stanford University, United States; Marc Rußwurm, Marco Körner, Technical University of Munich, Germany; David Lobell, Stanford University, United States