IEEE IGARSS 2021 || Brussels, Belgium || July 12–14, 2021 • Physical

TH1.O-15: DEEP Insight SAR III

Thu, 15 Jul, 08:30 - 10:00 (UTC)
Thu, 15 Jul, 10:30 - 12:00 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 16:30 - 18:00 China Standard Time (UTC +8)
Thu, 15 Jul, 04:30 - 06:00 Eastern Daylight Time (UTC -4)

Session Co-Chairs: Mihai Datcu, Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB) and Earth Observation Center (EOC), German Aerospace Center (DLR) and Zhongling Huang, Northwestern Polytechnical University

Session Manager: Saeed Khabbazan

Track: Invited Sessions

TH1.O-15.1: PHYSICS-AWARE FEATURE LEARNING OF SAR IMAGES WITH DEEP NEURAL NETWORKS: A CASE STUDY

Zhongling Huang, Northwestern Polytechnical University, China; Corneliu Octavian Dumitru, German Aerospace Center (DLR), Germany; Jun Ren, Institute of Mechanical and Electrical Engineering, China

TH1.O-15.3: PROPOSAL OF COMPLEX-VALUED RESERVOIR COMPUTING FOR TOPOGRAPHIC ASPECT CLASSIFICATION

Bungo Konishi, Akira Hirose, Ryo Natsuaki, University of Tokyo, Japan

TH1.O-15.4: INTEGRATION OF IEM B, ISMN AND SAR SENTINEL-1 DATA FOR ACCURATE SOIL MOISTURE ESTIMATION USING NEURAL NETWORKS

Leonardo De Laurentiis, University of Rome Tor Vergata, Italy; Daniele Latini, GEO-K s.r.l., Italy; Giovanni Schiavon, Fabio Del Frate, University of Rome Tor Vergata, Italy

TH1.O-15.5: THREE-DIMENSIONAL ASTEROID RECONSTRUCTION VIA MULTI-ASPECT GROUND-BASED SAR IMAGES: AN OPTIMIZATION COMPARISON

Yi Liu, Zegang Ding, Yan Wang, Tao Zeng, Zehua Dong, Beijing Institute of Technology, China

TH1.O-15.6: DERIVING AN EXCLUSION MAP (EX-MAP) FROM SENTINEL-1 TIME SERIES FOR SUPPORTING FLOODWATER MAPPING

Jie Zhao, Ramona Pelich, Renaud Hostache, Patrick Matgen, Luxembourg Institute of Science and Technology, Luxembourg; Senmao Cao, EODC Earth Observation Data Centre, Austria; Wolfgang Wagner, Vienna University of Technology, Austria; Marco Chini, Luxembourg Institute of Science and Technology, Luxembourg

TH1.O-15.1: PHYSICS-AWARE FEATURE LEARNING OF SAR IMAGES WITH DEEP NEURAL NETWORKS: A CASE STUDY

TH1.O-15.3: PROPOSAL OF COMPLEX-VALUED RESERVOIR COMPUTING FOR TOPOGRAPHIC ASPECT CLASSIFICATION

TH1.O-15.4: INTEGRATION OF IEM B, ISMN AND SAR SENTINEL-1 DATA FOR ACCURATE SOIL MOISTURE ESTIMATION USING NEURAL NETWORKS

TH1.O-15.5: THREE-DIMENSIONAL ASTEROID RECONSTRUCTION VIA MULTI-ASPECT GROUND-BASED SAR IMAGES: AN OPTIMIZATION COMPARISON

TH1.O-15.6: DERIVING AN EXCLUSION MAP (EX-MAP) FROM SENTINEL-1 TIME SERIES FOR SUPPORTING FLOODWATER MAPPING

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