MOP2.PH: Ship Detection

Session Type: Poster
Time: Monday, July 23, 15:50 - 16:50
Location: Poster Area H
Session Chair: Emmanuel Dinnat, Chapman University & NASA/GSFC
 
MOP2.PH.1: A LOCAL CFAR DETECTOR BASED ON GRAY INTENSITY CORRELATION IN SAR IMAGERY
         Jiaqiu Ai; Hefei University of Technology
         Xuezhi Yang; Hefei Univeristy of Technology
         He Yan; Nanjing University of Aeronautics and Astronautics
 
MOP2.PH.2: A PRIORI-KNOWLEDGE BASED SHIP CFAR DETECTION AND DETERMINATION ALGORITHM IN SAR IMAGERY
         Jiaqiu Ai; Hefei University of Technology
         Xuezhi Yang; Hefei University of Technology
         Zhihuo Xu; Nantong Univeristy
         Ruitian Tian; Hefei Univeristy of Technology
 
MOP2.PH.3: A NEW SCATTERING SIMILARITY BASED METRIC FOR SHIP DETECTION IN POL-SAR IMAGE
         Yunhong Tao; Beijing University of Chemical Technology
         Haitao Lang; Beijing University of Chemical Technology
         Hongji Shi; Beijing University of Chemical Technology
 
MOP2.PH.4: A SHIP DETECTOR BASED ON THE IMPROVED POLARIMETRIC COVARIANCE DIFFERENCE MATRIX
         Tao Zhang; Shanghai Jiao Tong University, KTH Royal Institute of Technology
         Yifang Ban; KTH Royal Institute of Technology
         Huilin Xiong; Shanghai Jiao Tong University
         Wenxian Yu; Shanghai Jiao Tong University
 
MOP2.PH.5: LAND MASKING METHOD FOR SAR-BASED SHIP DETECTION IN COASTAL WATERS OF MANY ISLANDS
         Chan-Su Yang; Korea Institute of Ocean Science and Technology
         Ju-Han Park; Korea Institute of Ocean Science and Technology
         Ahmed Harun-Al-Rashid; Korea Institute of Ocean Science and Technology
 
MOP2.PH.6: SHIP DETECTION WITHOUT SEA-LAND SEGMENTATION FOR LARGE-SCALE HIGH-RESOLUTION OPTICAL SATELLITE IMAGES
         Yiqun He; University of Chinese Academy of Sciences
         Xu Sun; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
         Lianru Gao; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
         Bing Zhang; Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
 
MOP2.PH.7: A NEW SAR IMAGE SIMULATION METHOD FOR SEA-SHIP SCENE
         Weibo Huo; University of Electronic Science and Technology of China
         Yulin Huang; University of Electronic Science and Technology of China
         Jifang Pei; University of Electronic Science and Technology of China
         Yin Zhang; University of Electronic Science and Technology of China
         Jianyu Yang; University of Electronic Science and Technology of China
 
MOP2.PH.8: SYNTHETIC APERTURE RADAR SHIP DETECTION USING CAPSULE NETWORKS
         Colin Schwegmann; Council For Scientific and Industrial Research
         Waldo Kleynhans; Council For Scientific and Industrial Research
         Brian Salmon; University of Tasmania
         Lizwe Mdakane; Council For Scientific and Industrial Research
         Rory Meyer; Council For Scientific and Industrial Research
 
MOP2.PH.9: SAR IMAGE SHIP DETECTION BASED ON YOLOV2 DEEP LEARNING FRAMEWORK
         Yang-Lang Chang; National Taipei University of Technology
         Chih-Yu Hsiao; National Taipei University of Technology
         Wei-Hong Lee; National Taipei University of Technology
         Amare Anagaw Ayele; National Taipei University of Technology
         Lena Chang; National Taiwan Ocean University
 
MOP2.PH.10: COMPREHENSIVE STRUCTURE VOTING DOCKED SHIP DETECTION FROM HIGH-RESOLUTION OPTICAL SATELLITE IMAGES BASED ON COMBINED MULTI-ORIENTATION SPARSE REPRESENTATION
         Yin Zhuang; Beijing Institute of Technology
         He Chen; Beijing Institute of Technology
         Haotian Zhou; Beijing Institute of Technology
         Liang Chen; Beijing Institute of Technology
         Fukun Bi; North China University of Technology