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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
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Session Detail
Session Title
TH2.R5: Data Fusion: SAR and Optical
Presentation Mode
Virtual
Session Time
Thu, 01 Oct, 14:30 - 16:30 UTC
Thu, 01 Oct, 22:30 - 00:30 China Standard Time (UTC +8)
Thu, 01 Oct, 16:30 - 18:30 Central Europe Time (UTC +2)
Thu, 01 Oct, 07:30 - 09:30 Pacific Daylight Time (UTC -7)
Session Chairs
Florence Tupin, Telecom ParisTech and Francesca Bovolo, Fondazione Bruno Kessler
TH2.R5.1:
CLOUD REMOVAL IN UNPAIRED SENTINEL-2 IMAGERY USING CYCLE-CONSISTENT GAN AND SAR-OPTICAL DATA FUSION
Patrick Ebel;
Technical University of Munich
Michael Schmitt;
Technical University of Munich
Xiaoxiang Zhu;
Technical University of Munich
TH2.R5.2:
GAN-BASED SAR-TO-OPTICAL IMAGE TRANSLATION WITH REGION INFORMATION
Kento Doi;
University of Tokyo
Ken Sakurada;
National Institute of Advanced Industrial Science and Technology
Masaki Onishi;
National Institute of Advanced Industrial Science and Technology
Akira Iwasaki;
University of Tokyo
TH2.R5.3:
SATELLITE DATA FUSION OF MULTIPLE OBSERVED XCO2 USING COMPRESSIVE SENSING AND DEEP LEARNING
Phuong Nguyen;
University Of Maryland Baltimore County
Samit Shivadekar;
University Of Maryland Baltimore County
Sai Sree Laya Chukkapalli;
University Of Maryland Baltimore County
Milton Halem;
University Of Maryland Baltimore County
TH2.R5.4:
SAR AND AIS DATA FUSION FOR DENSE SHIPPING ENVIRONMENTS
Maximilian Rodger;
University of Surrey
Raffaella Guida;
University of Surrey
TH2.R5.5:
ON THE FUSION STRATEGIES OF SENTINEL-1 AND SENTINEL-2 DATA FOR LOCAL CLIMATE ZONE CLASSIFICATION
Jakob Gawlikowski;
German Aerospace Center
Michael Schmitt;
Technical University of Munich
Anna Kruspe;
German Aerospace Center
Xiao Xiang Zhu;
German Aerospace Center
TH2.R5.6:
MULTI-POL SAR DATA FUSION FOR COASTLINE EXTRACTION BY NEURAL NETWORKS CHAINING
Leonardo De Laurentiis;
University of Rome Tor Vergata
Daniele Latini;
University of Rome Tor Vergata
Giovanni Schiavon;
University of Rome Tor Vergata
Fabio Del Frate;
University of Rome Tor Vergata
TH2.R5.7:
AUTOMATIC AREA-BASED REGISTRATION OF OPTICAL AND SAR IMAGES THROUGH GENERATIVE ADVERSARIAL NETWORKS AND A CORRELATION-TYPE METRIC
Luca Maggiolo;
University of Genoa
David Solarna;
University of Genoa
Gabriele Moser;
University of Genoa
Sebastiano Serpico;
University of Genoa
TH2.R5.8:
AUTOMATE LITHOLOGICAL CLASSIFICATION OF THE AMOTAPE TAHUIN METAMORPHIC COMPLEX IN ECUADOR USING RANDOM FOREST AND A MULTI-SENSOR SATELLITE IMAGERY APPROACH
Erith Muñoz;
FAO
Jhonatan Enriquez;
Instituto de Investigacion Geologico y Energetico
Daniel Toctaguano;
Instituto de Investigacion Geologico y Energetico
Mariaelisa Bustos;
Instituto de Investigacion Geologico y Energetico
Franz Betancourt;
Instituto de Investigacion Geologico y Energetico
Carmen Sangucho;
Instituto de Investigacion Geologico y Energetico
Victor Parra;
Instituto de Investigacion Geologico y Energetico
Aracely Lima;
Instituto de Investigacion Geologico y Energetico
Alfonso Zozaya;
Universidad Tecnológica Metropolitana
TH2.R5.9:
OPTICAL AND POLARIMETRIC SAR DATA FUSION TERRAIN CLASSIFICATION USING PROBABILISTIC FEATURE FUSION
R. Derek West;
Sandia National Laboratories
David Yocky;
Sandia National Laboratories
Brian Redman;
Sandia National Laboratories
John van der Laan;
Sandia National Laboratories
Dylan Anderson;
Sandia National Laboratories
TH2.R5.10:
SAR IMAGE SUPER-RESOLUTION BASE ON WEIGHTED DENSE CONNECTED CONVOLUTIONAL NETWORK
Jianwen Yu;
UESTC
Wenchao Li;
UESTC
Zhongyu Li;
UESTC
Junjie Wu;
UESTC
Haiguang Yang;
UESTC
Jianyu Yang;
UESTC
TH2.R5.11:
PYSRRESNET: SUPER RESOLUTION FOR VIDEO SATELLITE IMAGERY VIA PYRAMID RESIDUAL NETWORK
Man Xiao;
Sun Yat-sen University
Zhi He;
Sun Yat-sen University
Jiemin Wu;
Sun Yat-sen University