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

TH1.R5.2

SEMI-SUPERVISED CLASSIFICATION OF POLSAR DATA WITH MULTI-SCALE WEIGHTED GRAPH CONVOLUTIONAL NETWORK

Shijie Ren, Feng Zhou, Xidian University, China
Session:
Classification Methods for SAR Data
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Thu, 1 Oct, 12:10-12:20 (UTC)
Thu, 1 Oct, 20:10-20:20 China Standard Time (UTC +8)
Thu, 1 Oct, 14:10-14:20 Central Europe Summer Time (UTC +2)
Thu, 1 Oct, 05:10-05:20 Pacific Daylight Time (UTC -7)
Session Co-Chairs:
Mihai Datcu, German Aerospace Center (DLR) and Florence Tupin, Telecom ParisTech
Session Managers:
John Agbo Ogbodo and Seif Ammar

Presentation

Discussion

Resources

Session

TH1.R5.1: LAND COVER CLASSIFICATION FOR POLSAR IMAGES BASED ON MIXTURE MODELS AND MRF
Xiyun Liu, Junjun Yin, University of Science and Technology Beijing, China; Jihua Zhang, Shanghai Electro Mechanical Engineering Institute, China; Jian Yang, Tsinghua University, China
TH1.R5.2: SEMI-SUPERVISED CLASSIFICATION OF POLSAR DATA WITH MULTI-SCALE WEIGHTED GRAPH CONVOLUTIONAL NETWORK
Shijie Ren, Feng Zhou, Xidian University, China
TH1.R5.3: UNSUPERVISED LAND COVER CLASSIFICATION OF HYBRID POLSAR IMAGES USING DEEP NETWORK
Ankita Chatterjee, Jayasree Saha, Jayanta Mukhopadhyay, Subhas Aikat, Arundhati Misra, Indian Institute of Technology Kharagpur, India
TH1.R5.4: COMPLEX-VALUED SPATIAL-SCATTERING SEPARATED ATTENTION NETWORK FOR POLSAR IMAGE CLASSIFICATION
Zhaohao Fan, Zexuan Ji, Peng Fu, Tao Wang, Xiaobo Shen, Quansen Sun, Nanjing University of Science and Technology, China
TH1.R5.5: A HYBRID AND EXPLAINABLE DEEP LEARNING FRAMEWORK FOR SAR IMAGES
Zhongling Huang, Chinese Academy of Sciences, China; Mihai Datcu, German Aerospace Center, Germany; Zongxu Pan, Bin Lei, Chinese Academy of Sciences, China
TH1.R5.6: POLSAR SCENE CLASSIFICATION VIA LOW-RANK TENSOR-BASED MULTI-VIEW SUBSPACE REPRESENTATION
Mengqian Chen, Bo Ren, Biao Hou, Xidian University, China; Jocelyn Chanussot, University Grenoble Alpes, France; Shuang Wang, Xiangrong Zhang, Xidian University, China; Wen Xie, Xi'an University of Posts and Telecommunications, China
TH1.R5.7: POLSAR IMAGE CLASSIFICATION BASED ON OPTIMAL FEATURE AND CONVOLUTION NEURAL NETWORK
Ping Han, Zetao Chen, Yishuang Wan, Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, China; Zheng Cheng, Civil Aviation University of China, China
TH1.R5.8: ASSESSING FOREST/NON-FOREST SEPARABILITY USING SENTINEL-1 C-BAND SAR
Johannes N. Hansen, Edward T. A. Mitchard, Stuart King, University of Edinburgh, United Kingdom
TH1.R5.9: LEARNING RELATION BY GRAPH NEURAL NETWORK FOR SAR IMAGE FEW-SHOT LEARNING
Rui Yang, Xin Xu, Xirong Li, Lei Wang, Fangling Pu, Wuhan University, China
TH1.R5.10: A NEURAL NETWORK APPROACH TO CLASSIFY MIXED CLASSES USING MULTI FREQUENCY SAR DATA
Anjana Kukunuri, Deepak Murugan, Dharmendra Singh, Indian Institute of Technology Roorkee, India
TH1.R5.11: STACKED RANDOM FORESTS: MORE ACCURATE AND BETTER CALIBRATED
Ronny Hänsch, German Aerospace Center (DLR), Germany
TH1.R5.12: MULTI-VIEW CNN-LSTM NEURAL NETWORK FOR SAR AUTOMATIC TARGET RECOGNITION
Chenwei Wang, Jifang Pei, Zhiyong Wang, Yuling Huang, Jianyu Yang, UESTC, China