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International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TH2.MM-10.5

MULTI-SCALE META-LEARNING-BASED NETWORKS FOR HIGH-RESOLUTION REMOTE SENSING SCENE CLASSIFICATION

Xu Tang, Weiquan Lin, Chao Liu, Xidian University, China; Xiao Han, Geovis Spatial Technology Co.,Ltd, China; Wenjing Wang, Science and Technology on Electro-optic Control Laboratory, China; Jingjing Ma, Licheng Jiao, Xidian University, China
Session:
Scene Classification and Recognition
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Thu, 15 Jul, 11:20-11:25 (UTC)
Thu, 15 Jul, 13:20-13:25 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 19:20-19:25 China Standard Time (UTC +8)
Thu, 15 Jul, 07:20-07:25 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
lichao mou, German Aerospace Center & Technical University of Munich and Gabriele Moser, University of Genoa
Session Manager:
Zhendong Lu
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Session TH2.MM-10
TH2.MM-10.1: REMOTE SCENE IMAGE SCENE CLASSIFICATION BASED ON ADAPTIVE SEGMENTATION AND DYNAMIC GRAPH CONVOLUTION
Yuqun Yang, Xu Tang, Xidian University, China; Xiao Han, Geovis Spatial Technology Co.,Ltd, China; Jingjing Ma, Xiangrong Zhang, Licheng Jiao, Xidian University, China
TH2.MM-10.2: A MULTI-SCALE FEATURE AGGREGATION NETWORK BASED ON CHANNEL-SPATIAL ATTENTION FOR REMOTE SENSING SCENE CLASSIFICATION
Ming Li, Lin Lei, Xiao Li, Yuli Sun, National University of Defence Technology, China
TH2.MM-10.3: CNN-GCN JOINT NETWORK FOR REMOTE SENSING SCENE CLASSIFICATION
Kejie Xu, Hong Huang, Peifang Deng, Chongqing University, China
TH2.MM-10.4: MEMORY USING DATA GENERATOR IN CONTINUAL LEARNING FOR REMOTE SENSING SCENE CLASSIFICATION
Nassim Ammour, King Saud University, Saudi Arabia
TH2.MM-10.5: MULTI-SCALE META-LEARNING-BASED NETWORKS FOR HIGH-RESOLUTION REMOTE SENSING SCENE CLASSIFICATION
Xu Tang, Weiquan Lin, Chao Liu, Xidian University, China; Xiao Han, Geovis Spatial Technology Co.,Ltd, China; Wenjing Wang, Science and Technology on Electro-optic Control Laboratory, China; Jingjing Ma, Licheng Jiao, Xidian University, China
TH2.MM-10.6: A SEMI-SUPERVISED SIAMESE NETWORK WITH LABEL FUSION FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION
Wang Miao, Jie Geng, Xinyang Deng, Wen Jiang, Northwestern Polytechnical University, China
TH2.MM-10.7: ROBUST REMOTE SENSING SCENE CLASSIFICATION BY ADVERSARIAL SELF-SUPERVISED LEARNING
Yanjie Xu, Hao Sun, National University of Defence Technology, China; Jin Chen, Beijing Institute of Remote Sensing Information, China; Lin Lei, Gangyao Kuang, Kefeng Ji, National University of Defence Technology, China
TH2.MM-10.8: MULTI-OBJECTIVE NET ARCHITECTURE PRUNING FOR REMOTE SENSING CLASSIFICATION
Jiaqi Zhao, Chengrun Yang, Yong Zhou, Yajie Zhou, Zhujun Jiang, Ying Chen, China University of Mining and Technology, China
TH2.MM-10.9: NATURAL SCENE RECOGNITION BASED ON HRRP STATISTICAL MODELING
Shu-Qi Lei, Fudan University, China; Dong-Xiao Yue, Shanghai Maritime University, China; Feng Wang, Key Laboratory for Information Science of Electromagnetic Waves (MoE), China
TH2.MM-10.10: Unconstrained Aerial Scene Recognition with Deep Neural Networks and A New Dataset
Yuansheng Hua, Lichao Mou, German Aerospace Center & Technical University of Munich, Germany; Pu Jin, Technical University of Munich, Germany; Xiao Xiang Zhu, German Aerospace Center & Technical University of Munich, Germany

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