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TU2.L2: Deep Networks for Detection and Recognition I

Session Type: Oral
Time: Tuesday, July 25, 10:40 - 12:20
Location: Ballroom A
Session Chair: Leyuan Fang, Hunan University
 
  TU2.L2.1: DEEP CONVOLUTIONAL NEURAL NETWORK BASED LARGE-SCALE OIL PALM TREE DETECTION FOR HIGH-RESOLUTION REMOTE SENSING IMAGES
         Weijia Li; Tsinghua University
         Haohuan Fu; Tsinghua University
         Le Yu; Tsinghua University
 
  TU2.L2.2: EVALUATION THE PERFORMANCE OF FULLY CONVOLUTIONAL NETWORKS FOR BUILDING EXTRACTION COMPARED WITH SHALLOW MODELS
         Youyou Li; University of Electronic Science and Technology of China
         Binbin He; University of Electronic Science and Technology of China
         Teng Long; University of Electronic Science and Technology of China
         Xiaojing Bai; University of Electronic Science and Technology of China
 
  TU2.L2.3: HIERARCHICAL FEATURE EXTTRATCTION FOR OBJECT RECOGITION IN COMPLEX SAR IMAGE USING MODIFIED CONVOLUTIONAL AUTO-ENCODER
         Sirui Tian; Nanjing University of Science and Technology
         Chao Wang; Chinese Academy of Sciences
         Hong Zhang; Chinese Academy of Sciences
 
  TU2.L2.4: FAST MULTICLASS OBJECT DETECTION IN OPTICAL REMOTE SENSING IMAGES USING REGION BASED CONVOLUTIONAL NEURAL NETWORKS
         Zhipeng Deng; National University of Defense Technology
         Hao Sun; National University of Defense Technology
         Shilin Zhou; National University of Defense Technology
         Juanping Zhao; Shanghai Jiao Tong University
         Lin Lei; National University of Defense Technology
         Huanxin Zou; National University of Defense Technology
 
  TU2.L2.5: TRAINING DEEP CONVOLUTION NEURAL NETWORK WITH HARD EXAMPLE MINING FOR AIRPORT DETECTION
         Bowen Cai; Beihang University
         Zhiguo Jiang; Beihang University
         Haopeng Zhang; Beihang University
         Yuan Yao; Beihang University
         Jie Huang; Beihang University
 
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