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

My IGARSS 2020 Schedule

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Session Detail

Session Title TH1.R9: Semantic Learning for Image Analysis
Presentation Mode Virtual
Session Time Thu, 01 Oct, 12:00 - 14:00 UTC
Thu, 01 Oct, 20:00 - 22:00 China Standard Time (UTC +8)
Thu, 01 Oct, 14:00 - 16:00 Central Europe Time (UTC +2)
Thu, 01 Oct, 05:00 - 07:00 Pacific Daylight Time (UTC -7)
Session ChairsStefano Zorzi, TUGraz and Bohao Huang, Duke University
TH1.R9.1: SELF-CONSTRUCTING GRAPH CONVOLUTIONAL NETWORKS FOR SEMANTIC LABELING
         Qinghui Liu; Norwegian Computing Center
         Michael Kampffmeyer; UiT The Arctic University of Norway
         Robert Jenssen; UiT The Arctic University of Norway
         Arnt-Børre Salberg; Norwegian Computing Center
TH1.R9.2: REGULARIZED BUILDING SEGMENTATION BY FRAME FIELD LEARNING
         Nicolas Girard; Inria
         Dmitriy Smirnov; Massachusetts Institute of Technology
         Justin Solomon; Massachusetts Institute of Technology
         Yuliya Tarabalka; LuxCarta Technology
TH1.R9.3: LOOK AT THE BIG PICTURE: BUILDING AREA EXTRACTION WITH GLOBAL DENSITY MAP
         Haowen Guo; Wuhan University
         Tongyuan Zou; Space Star Technology Co., Ltd. (SST)
         Wensheng Cheng; Wuhan University
         Wen Yang; Wuhan University
         Guisong Xia; Wuhan University
TH1.R9.4: SEMANTIC SEGMENTATION REFINEMENT WITH DEEP EDGE SUPERPIXELS TO ENHANCE HISTORICAL LAND COVER
         Rémi Ratajczak; Laboratoire d'InfoRmatique en Image et Systèmes d'information
         Carlos Crispim-Junior; Laboratoire d'InfoRmatique en Image et Systèmes d'information
         Béatrice Fervers; Centre Léon Bérard
         Elodie Faure; Gustave Roussy
         Laure Tougne; Laboratoire d'InfoRmatique en Image et Systèmes d'information
TH1.R9.5: A MODIFIED D-LINKNET WITH TRANSFER LEARNING FOR ROAD EXTRACTION FROM HIGH-RESOLUTION REMOTE SENSING
         Yanan Zhang; China University of Geosciences
         Qiqi Zhu; China University of Geosciences
         Yanfei Zhong; Wuhan University
         Qingfeng Guan; China University of Geosciences
         Liangpei Zhang; Wuhan University
         Deren Li; Wuhan University
TH1.R9.6: LEARNING DISCRIMINATIVE GLOBAL AND LOCAL FEATURES FOR BUILDING EXTRACTION FROM AERIAL IMAGES
         Yue Liao; Wuhan University
         Hongyan Zhang; Wuhan University
         Guangyi Yang; Wuhan University
         Liangpei Zhang; Wuhan University
TH1.R9.7: DILATED RESIDUAL NETWORK BASED ON DUAL EXPECTATION MAXIMIZATION ATTENTION FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES
         Jiachao Liu; Xidian University
         Xinyue Xiong; Xidian University
         Jiaojiao Li; Xidian University
         Chaoxiong Wu; Xidian University
         Rui Song; Xidian University
TH1.R9.8: MAP-REPAIR: DEEP CADASTRE MAPS ALIGNMENT AND TEMPORAL INCONSISTENCIES FIX IN SATELLITE IMAGES
         Stefano Zorzi; TUGraz
         Ksenia Bittner; German Aerospace Center (DLR)
         Friedrich Fraundorfer; TUGraz
TH1.R9.9: SEMANTIC SEGMENTATION OF URBAN BUILDINGS FROM VHR REMOTELY SENSED IMAGERY USING ATTENTION-BASED CNN
         Zhijie Zhang; University of Connecticut
         Chuanrong Zhang; University of Connecticut
         Weidong Li; University of Connecticut
TH1.R9.10: SEMI2I: SEMANTICALLY CONSISTENT IMAGE-TO-IMAGE TRANSLATION FOR DOMAIN ADAPTATION OF REMOTE SENSING DATA
         Onur Tasar; INRIA
         S L Happy; INRIA
         Yuliya Tarabalka; INRIA
         Pierre Alliez; INRIA
TH1.R9.11: SPATIAL ATTENTION NETWORK FOR ROAD EXTRACTION
         Ruonan Chen; Aerospace Information Research Institute, Chinese Academy of Sciences
         Yuan Hu; Aerospace Information Research Institute, Chinese Academy of Sciences
         Tong Wu; Aerospace Information Research Institute, Chinese Academy of Sciences
         Ling Peng; Aerospace Information Research Institute, Chinese Academy of Sciences