TU2.L2: Feature Extraction and Application

Session Type: Oral
Time: Tuesday, July 12, 10:30 - 12:10
Location: Room 311B
Session Chair: Melba Crawford, Purdue University
 
10:30 - 10:50
TU2.L2.1: FEATURE EXTRACTION AND CLASSIFICATION OF OCEAN OIL SPILL BASED ON SAR IMAGE
         Peng Zhao; University of Electronic Science and Technology of China
         Xun Yang; University of Electronic Science and Technology of China
         Yan Chen; University of Electronic Science and Technology of China
         Ling Tong; University of Electronic Science and Technology of China
         Lei He; University of Electronic Science and Technology of China
 
10:50 - 11:10
TU2.L2.2: MACHINE-LEARNING BASED DETECTION OF CORRESPONDING INTEREST POINTS IN OPTICAL AND SAR IMAGES
         Ronny Hänsch; Technische Universität Berlin
         Olaf Hellwich; Technische Universität Berlin
         Xiaohong Tu; Technische Universität Berlin
 
11:10 - 11:30
TU2.L2.3: HYPERSPECTRAL IMAGE SUPERVISED CLASSIFICATION VIA MULTI-VIEW NUCLEAR NORM BASED 2D PCA FEATURE EXTRACTION AND KERNEL ELM
         Jue Jiang; Nanjing University of Science and Technology
         Lili Huang; Guangxi University of Science and Technology
         Heng Li; Nanjing University of Science and Technology
         Liang Xiao; Nanjing University of Science and Technology
 
11:30 - 11:50
TU2.L2.4: EFFICIENT SEMI-SUPERVISED FEATURE SELECTION FOR VHR REMOTE SENSING IMAGES
         Xi Chen; Harbin Institute of Technology
         Lin Song; Harbin Institute of Technology
         Wei Liu; Harbin Institute of Technology
         Yuguan Hou; Harbin Institute of Technology
         Ye Zhang; Harbin Institute of Technology
         Guofan Shao; Purdue University
 
11:50 - 12:10
TU2.L2.5: FEATURE EXTRACTION AND TRACKING FOR LARGE-SCALE GEOSPATIAL DATA
         Lina Yu; University of Nebraska-Lincoln
         Feiyu Zhu; University of Nebraska-Lincoln
         Hongfeng Yu; University of Nebraska-Lincoln
         Jun Wang; University of Nebraska-Lincoln
         Kwo-Sen Kuo; University of Maryland