MOP2.PG: Applications of Deep Learning

Session Type: Poster
Time: Monday, July 23, 15:50 - 16:50
Location: Poster Area G
Session Chairs: Emanuele Santi, CNR-IFAC and Xuiping Jia, University of New South Wales at Canberra
 
MOP2.PG.1: OIL-PALM TREE DETECTION IN AERIAL IMAGES COMBINING DEEP LEARNING CLASSIFIERS
         Maciel Zortea; IBM Research
         Marcelo Nery; IBM Research
         Bernardo Ruga; IBM Research
         Lara Bispo Carvalho; Agropalma
         Adriano Chaves Bastos; Agropalma
 
MOP2.PG.2: SPECTRAL-SPATIAL TOPOGRAPHIC SHADOW DETECTION FROM SENTINEL-2A MSI IMAGERY VIA CONVOLUTIONAL NEURAL NETWORKS
         Hui Huang; China University of Petroleum (East China)
         Genyun Sun; China University of Petroleum (East China)
         Jinchang Ren; University of Strathclyde
         Jun Rong; China University of Petroleum (East China)
         Aizhu Zhang; China University of Petroleum (East China)
         Yanling Hao; China University of Petroleum (East China)
 
MOP2.PG.3: AGGREGATING DEEP CONVOLUTIONAL NEURAL NETWORK SCANS OF BROAD-AREA HIGH-RESOLUTION REMOTE SENSING IMAGERY
         Grant Scott; University of Missouri
         Alex Hurt; University of Missouri
         Richard Marcum; University of Missouri
         Derek Anderson; University of Missouri
         Curt Davis; University of Missouri
 
MOP2.PG.4: AN ADAPTATION OF CNN FOR SMALL TARGET DETECTION IN THE INFRARED
         Dong Zhao; Xidian University
         Huixin Zhou; Xidian University
         Shenghui Rong; Xidian University
         Xiuping Jia; University of New South Wales
 
MOP2.PG.5: ROTATED REGION BASED FULLY CONVOLUTIONAL NETWORK FOR SHIP DETECTION
         Mingjie Li; Shanghai Jiao Tong University
         Weiwei Guo; Shanghai Jiao Tong University
         Zenghui Zhang; Shanghai Jiao Tong University
         Wenxian Yu; Shanghai Jiao Tong University
         Tao Zhang; Shanghai Jiao Tong University
 
MOP2.PG.6: A TRANSLATIONAL INVARIANT SAR-ATR METHOD BASED ON CONVOLUTIONAL NEURAL NETWORKS
         Zongyong Cui; University of Electronic Science and Technology of China
         Sifei Wang; University of Electronic Science and Technology of China
         Sihang Dang; University of Electronic Science and Technology of China
         Zongjie Cao; University of Electronic Science and Technology of China
 
MOP2.PG.7: SHIP DETECTION BASED ON DEEP CONVOLUTIONAL NEURAL NETWORKS FOR POLSAR IMAGES
         Feng Zhou; Xidian University
         Weiwei Fan; Xidian University
         Qiangqiang Sheng; Xidian University
         Mingliang Tao; Northwestern Polytechnical University
 
MOP2.PG.8: NARROW ROAD EXTRACTION FROM REMOTE SENSING IMAGES BASED ON SUPER-RESOLUTION CONVOLUTIONAL NEURAL NETWORK
         Xinyu Zhou; Harbin Institute of Technology
         Xi Chen; Key Laboratory of Geographic Information Science (Ministry of Education)
         Ye Zhang; Harbin Institute of Technology
 
MOP2.PG.9: DATA AUGMENTATION WITH GABOR FILTER IN DEEP CONVOLUTIONAL NEURAL NETWORKS FOR SAR TARGET RECOGNITION
         Ting Jiang; University of Electronic Science and Technology of China
         Zongyong Cui; University of Electronic Science and Technology of China
         Zhi Zhou; University of Electronic Science and Technology of China
         Zongjie Cao; University of Electronic Science and Technology of China
 
MOP2.PG.10: INSHORE SHIP DETECTION BASED ON MASK R-CNN
         Shanlan Nie; Beihang University
         Zhiguo Jiang; Beihang University
         Haopeng Zhang; Beihang University
         Bowen Cai; Beihang University
         Yuan Yao; Beihang University