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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
MO2.R17: Detection of Small Static and Moving Objects
Presentation Mode
Virtual
Session Time
Mon, 28 Sep, 14:30 - 16:30 UTC
Mon, 28 Sep, 22:30 - 00:30 China Standard Time (UTC +8)
Mon, 28 Sep, 16:30 - 18:30 Central Europe Time (UTC +2)
Mon, 28 Sep, 07:30 - 09:30 Pacific Daylight Time (UTC -7)
Session Chairs
Yiyou Guo, Tongji University and Nathan Longbotham, Descartes Lab
MO2.R17.1:
VEHICLE DETECTION AND COUNTING FROM VHR SATELLITE IMAGES: EFFORTS AND OPEN ISSUES
Alice Froidevaux;
QuantCube Technology
Andréa Julier;
QuantCube Technology
Agustin Lifschitz;
QuantCube Technology
Minh-Tan Pham;
Université Bretagne Sud - IRISA
Romain Dambreville;
Université Bretagne Sud - IRISA
Sébastien Lefèvre;
Université Bretagne Sud - IRISA
Pierre Lassalle;
Centre National d'Etudes Spatiales (CNES)
Thanh-Long Huynh;
QuantCube Technology
MO2.R17.2:
SMALL OBJECT DETECTION FROM REMOTE SENSING IMAGES WITH THE HELP OF OBJECT-FOCUSED SUPER-RESOLUTION USING WASSERSTEIN GANS
Luc Courtrai;
Univ. Bretagne Sud-IRISA
Minh-Tan Pham;
Univ. Bretagne Sud-IRISA
Chloé Friguet;
Univ. Bretagne Sud-IRISA
Sébastien Lefèvre;
Univ. Bretagne Sud-IRISA
MO2.R17.3:
AIRPLANE RECOGNITION FROM REMOTE SENSING IMAGES WITH DEEP CONVOLUTIONAL NEURAL NETWORK
Fen Chen;
University of Electronic Science and Technology of China
Ruilong Ren;
University of Electronic Science and Technology of China
Wenbo Xu;
University of Electronic Science and Technology of China
Tim Van de Voorde;
Ghent University
MO2.R17.4:
VEHSAT: A LARGE-SCALE DATASET FOR VEHICLE DETECTION IN SATELLITE IMAGES
Sebastien Drouyer;
ENS Paris Saclay
MO2.R17.5:
SMALL OBJECT DETECTION IN OPTICAL REMOTE SENSING VIDEO WITH MOTION GUIDED R-CNN
Jie Feng;
Xidian University
Yuping Liang;
Xidian University
Zhanwei Ye;
Xidian University
Xiande Wu;
Xidian University
Dening Zeng;
Xidian University
Xiangrong Zhang;
Xidian University
Xu Tang;
Xidian University
MO2.R17.6:
CONCURRENT SEGMENTATION AND OBJECT DETECTION CNNS FOR AIRCRAFT DETECTION AND IDENTIFICATION IN SATELLITE IMAGES
Damien Grosgeorge;
Earthcube
Maxime Arbelot;
Earthcube
Alex Goupilleau;
Earthcube
Tugdual Ceillier;
Earthcube
Renaud Allioux;
Earthcube
MO2.R17.7:
GEOSPATIAL OBJECT DETECTION WITH SINGLE SHOT ANCHOR-FREE NETWORK
Yiyou Guo;
Tongji University
Jinsheng Ji;
Shanghai Jiao Tong University
Xiankai Lu;
Inception Institute of Artificial Intelligence
Huan Xie;
Tongji University
Xiaohua Tong;
Tongji University
MO2.R17.8:
IMPROVING SAR TARGET RECOGNITION WITH MULTI-TASK LEARNING
Wenrui Du;
Beijing University of Chemical Technology
Fan Zhang;
Beijing University of Chemical Technology
Fei Ma;
Beijing University of Chemical Technology
Qiang Yin;
Beijing University of Chemical Technology
Yongsheng Zhou;
Beijing University of Chemical Technology
MO2.R17.9:
VEHICLE DETECTION WITH PARTIAL ANCHORS IN REMOTE SENSING IMAGES
Fuyan Ma;
Hunan University
Bin Sun;
Hunan University
Shutao Li;
Hunan University
Jun Sun;
Fujitsu Research and Develop Center
MO2.R17.10:
WEAK TARGET DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES BY COMBINING SUPER-RESOLUTION AND DEFORMABLE FPN
Yang Bai;
Harbin Institute of Technology
Tongyuan Zou;
Space Star Technology Co., Ltd. (SST)
Shujia Ye;
Harbin Institute of Technology
Zhenqiang Qin;
Harbin Institute of Technology
Guoming Gao;
Harbin Institute of Technology
Yanfeng Gu;
Harbin Institute of Technology
MO2.R17.11:
VESSEL TARGET MONITORING WITH BISTATIC COMPACT HF SURFACE WAVE RADAR
Yonggang Ji;
First Institute of Oceanography, Ministry of Natural Resources
Jie Zhang;
First Institute of Oceanography, Ministry of Natural Resources
Yiming Wang;
First Institute of Oceanography, Ministry of Natural Resources
Junmin Meng;
First Institute of Oceanography, Ministry of Natural Resources
Changjun Yu;
Harbin Institute of Technology at Weihai
Ming Li;
Ocean University of China
Weifeng Sun;
China University of Petroleum