TH3.M1.4
Multi-Domain Spatio-Temporal Feature Representation with Graph Neural Networks for Radar Target Detection
Junjie Wang, Dongying Li, Wenxian Yu, Shanghai Jiao Tong university, China
Session:
TH3.M1: Classification and Clustering of Radar and LiDAR Data Oral
Track:
Data Analysis
Location:
Room M1
Presentation Time:
Thursday, 7 August, 14:00 - 14:15
Presentation
Discussion
Resources
No resources available.
Session TH3.M1
TH3.M1.1: GROUND HRRP TARGET RECOGNITION UNDER LOW SNR BASED ON MSFF-CNN MODEL
Xiaohui Wei, Zhulin Zong, University of Electronic Science and Technology of China, China
TH3.M1.2: SPHERICAL FEATURES FOR SAR SHIP CLASSIFICATION WITH ADVERSARIAL DOMAIN ADAPTATION
Zhichao Han, Haitao Lang, Wangkai Luo, Beijing University of Chemical Technology, China
TH3.M1.3: A RADAR SIGNAL SORTING METHOD TO REDUCE BATCH EXPANSION
Haoyang Yu, Yujie Zhang, Weibo Huo, Yulin Huang, Jifang Pei, Yin Zhang, University of Electronic Science and Technology of China, China
TH3.M1.4: Multi-Domain Spatio-Temporal Feature Representation with Graph Neural Networks for Radar Target Detection
Junjie Wang, Dongying Li, Wenxian Yu, Shanghai Jiao Tong university, China
TH3.M1.5: Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution
Jian Yang, Binhan Luo, Ruilin Gan, Ao Wang, China University of Geosciences, China; Shuo Shi, Wuhan University, China; Lin Du, China University of Geosciences, China
Resources
No resources available.