Technical Program: Paper Detail || IEEE IGARSS 2023 || Pasadena, California, USA || 16

TH1.R14.4

IS REALISTIC SYNTHETIC INSAR DATA GENERATION FEASIBLE ON LOW DATA REGIMES?

Nikolaos Ioannis Bountos, Ioannis Papoutsis, Orion Lab, Institute for Astronomy, Astrophysics, Space Applications, and Remote Sensing, National Observatory of Athens, Greece; Dimitrios Michail, Harokopio University of Athens, Greece

Session:

TH1.R14: SAR and Machine Learning Oral

Location:

Room E

Presentation Time:

Thu, 20 Jul, 09:06 - 09:18 Pacific Time (UTC -7)

Session Co-Chairs:

Ronny Hänsch, German Aerospace Center and Kelsey Doerksen, University of Oxford

Session Manager:

Ayoti Banerjee

Session TH1.R14

TH1.R14.1: SALIENCY-GUIDED ATTENTION-BASED FEATURE PYRAMID NETWORK FOR SHIP DETECTION IN SAR IMAGES

Tianwen Zhang, Xiaoling Zhang, Zikang Shao, University of Electronic Science and Technology of China, China

TH1.R14.2: A HIGH ACCURACY DETECTION NETWORK FOR ROTATED MULTI-CLASS SAR SHIP DETECTION

Zikang Shao, Xiaoling Zhang, University of Electronic Science and Technology of China, China

TH1.R14.3: Automatic simulation of SAR images: comparing a deep-learning based method to a hybrid method.

Nathan Letheule, Flora Weissgerber, ONERA, France; Sylvain Lobry, LIPADE, Université de Paris, Paris, France, France; Elise Colin, ONERA, France

TH1.R14.4: IS REALISTIC SYNTHETIC INSAR DATA GENERATION FEASIBLE ON LOW DATA REGIMES?

TH1.R14.5: PRECIPITATION-TRIGGERED LANDSLIDE PREDICTION IN NEPAL USING MACHINE LEARNING AND DEEP LEARNING

Kelsey Doerksen, Yarin Gal, Freddie Kalaitzis, University of Oxford, United Kingdom; Cristian Rossi, Satellite Applications Catapult, United Kingdom; David Petit, Deimos Space, United Kingdom; Sihan Li, University of Sheffield, United Kingdom; Simon Dadson, University of Oxford, United Kingdom