IEEE IGARSS 2021 || Brussels, Belgium || July 12–14, 2021 • Physical

TU1.O-15.5

PROPOSAL OF POLSAR LAND CLASSIFICATION USING QUATERNION CONVOLUTIONAL NEURAL NETWORKS

Yuya Matsumoto, Ryo Natsuaki, Akira Hirose, University of Tokyo, Japan

Session:

DEEP Insight SAR I

Track:

Invited Sessions

Presentation Time:

Tue, 13 Jul, 09:30-09:45 (UTC)
Tue, 13 Jul, 11:30-11:45 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 17:30-17:45 China Standard Time (UTC +8)
Tue, 13 Jul, 05:30-05:45 Eastern Daylight Time (UTC -4)

Session Co-Chairs:

Mihai Datcu, Research Center for Spatial Information (CEOSpaceTech), University POLITEHNICA of Bucharest (UPB) and Earth Observation Center (EOC), German Aerospace Center (DLR) and Zhongling Huang, Northwestern Polytechnical University

Session Manager:

Lynette Dias

Session TU1.O-15

TU1.O-15.1: CAN WE EVALUATE THE DISTINGUISHABILITY OF THE OPENSARURBAN DATASET ?

Ning Liao, Shanghai Jiao Tong University, China; Mihai Datcu, German Aerospace Center (DLR), Germany; Zenghui Zhang, Shanghai Jiao Tong University, China; Weiwei Guo, Tongji University, China; Wenxian Yu, Shanghai Jiao Tong University, China

TU1.O-15.3: NONCOHERENT IMAGING EXPERIMENTS OF CIRCULAR SAR

Yuxiao Luo, Daoxiang An, Leping Chen, Jingwei Chen, Xiaotao Huang, National University of Defence Technology, China

TU1.O-15.4: SELF-CALIBRATED CONVOLUTIONAL NEURAL NETWORK FOR SAR IMAGE DESPECKLING

Ye Yuan, Yan Jiang, Yanxia Wu, Harbin Engineering University, China; Richard Jiang, Lancaster University, United Kingdom

TU1.O-15.5: PROPOSAL OF POLSAR LAND CLASSIFICATION USING QUATERNION CONVOLUTIONAL NEURAL NETWORKS

Yuya Matsumoto, Ryo Natsuaki, Akira Hirose, University of Tokyo, Japan

TU1.O-15.6: A DEEP FEATURE TRANSFORMATION METHOD BASED ON DIFFERENTIAL VECTOR FOR FEW-SHOT LEARNING

Qian Guo, Feng Xu, Fudan University, China

PROPOSAL OF POLSAR LAND CLASSIFICATION USING QUATERNION CONVOLUTIONAL NEURAL NETWORKS

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