WE1.R18.1: SHIPDENET-18: AN ONLY 1 MB WITH ONLY 18 CONVOLUTION LAYERS LIGHT-WEIGHT DEEP LEARNING NETWORK FOR SAR SHIP DETECTION
Tianwen Zhang, Xiaoling Zhang, Jun Shi, Shunjun Wei, University of Electronic Science and Technology of China, China
WE1.R18.2: AN INTEGRATED METHOD OF SHIP DETECTION AND RECOGNITION IN SAR IMAGES BASED ON DEEP LEARNING
Zesheng Hou, Zongyong Cui, Zongjie Cao, Nengyuan Liu, University of Electronic Science and Technology of China, China
WE1.R18.3: SHIP DETECTION IN RADAR IMAGE SERIES BASED ON THE LONG SHORT-TERM MEMORY NETWORK
Yi Xu, Bing Sun, Chunsheng Li, Jie Chen, Beihang University, China
WE1.R18.7: Dense Docked Ship Detection via Spatial Group-wise Enhance Attention in SAR Images
Xiaoya Wang, Zongyong Cui, Zongjie Cao, Sihang Dang, University of Electronic Science and Technology of China, China
WE1.R18.8: SHIP TARGET SIGNATURE INDICATION BASED ON COMPLEX SIGNAL KURTOSIS IN SAR IMAGES
Xiangguang Leng, Kefeng Ji, Boli Xiong, Gangyao Kuang, National University of Defense Technology, China
WE1.R18.9: A SVA BASED SIDELOBE SUPPRESSION METHOD FOR SEA-LAND SEGMENTATION AND SHIP DETECTION IN SAR IMAGES
Yinli Huang, Xidian University, China; Lu Sun, 93128 Troops of the Chinese peoples's liberation army, China; Liang Guo, Guangcai Sun, Mengdao Xing, Xidian University, China; Jun Yang, Xi’an University of Science and Technology, China; Yihua Hu, National University of Defense Technology, China
WE1.R18.10: SHIP DETECTION FROM POLSAR IMAGERY BASED ON THE SCATTERING DIFFERENCE PARAMETER
Tao Zhang, Tsinghua University, China; Zhen Yang, Jiangxi Science and Technology Normal University, China; Cheng Xing, Liang Zeng, Tsinghua University, China; Junjun Yin, University of Science and Technology Beijing, China; Jian Yang, Tsinghua University, China
WE1.R18.12: Ship Detection in Large Scale SAR Images Based on Bias Classification
Xiaoya Wang, Zongyong Cui, Zongjie Cao, Yu Tian, University of Electronic Science and Technology of China, China