TU2.R5: Hyperspectral Image Classification II

Tue, 29 Sep, 14:30 - 16:30 (UTC)
Tue, 29 Sep, 22:30 - 00:30 China Standard Time (UTC +8)
Tue, 29 Sep, 16:30 - 18:30 Central Europe Summer Time (UTC +2)
Tue, 29 Sep, 07:30 - 09:30 Pacific Daylight Time (UTC -7)
Session Co-Chairs: Licheng Jiao, Xidian University and Qian Du, Mississippi State University
Session Managers: Subhadip Dey and Shivam Pande
Track: Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
14:30-14:40 (UTC)
22:30-22:40 (UTC +8)
16:30-16:40 (UTC +2)
07:30-07:40 (UTC -7)

TU2.R5.1: Two-step ensemble based class noise cleaning method for hyperspectral image classification

Wei Feng, Yinghui Quan, School of Electronic Engineering, Xidian University, China; Gabriel Dauphin, Institut Galilée, University Paris XIII, France; Xian Zhong, School of Electronic Engineering, Xidian University, China; Qiang Li, Northwestern Polytechnical University, China; Mengdao Xing, Xidian University, China; Wenjiang Huang, Chinese Academy of Sciences, China
14:40-14:50 (UTC)
22:40-22:50 (UTC +8)
16:40-16:50 (UTC +2)
07:40-07:50 (UTC -7)

TU2.R5.2: A SUPERPIXEL-BASED FRAMEWORK FOR NOISY HYPERSPECTRAL IMAGE CLASSIFICATION

Peng Fu, Quansen Sun, Zexuan Ji, Nanjing University of Science and Technology, China; Leilei Geng, Shandong University of Finance and Economics, China
14:50-15:00 (UTC)
22:50-23:00 (UTC +8)
16:50-17:00 (UTC +2)
07:50-08:00 (UTC -7)

TU2.R5.3: HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON MULTISCALE SPATIAL AND SPECTRAL FEATURE NETWORK

Xu Tang, Fanbo Meng, Jingjing Ma, Xiangrong Zhang, Xidian University, China; Fang Liu, Nanjing University of Science and Technology, China; Qunnie Peng, Science and Technology on Electro-optic Control Laboratory, China; Licheng Jiao, Xidian University, China
15:00-15:10 (UTC)
23:00-23:10 (UTC +8)
17:00-17:10 (UTC +2)
08:00-08:10 (UTC -7)

TU2.R5.4: IMPROVING HYPERSPECTRAL IMAGE CLASSIFICATION USING GRAPH WAVELETS

Qipeng Qian, Shanghai Jiao Tong University, China; Xiaotian Fan, Zhejiang University, China; Minchao Ye, China Jiliang Universit, China
15:10-15:20 (UTC)
23:10-23:20 (UTC +8)
17:10-17:20 (UTC +2)
08:10-08:20 (UTC -7)

TU2.R5.5: JOINT GROUP SPARSE COLLABORATIVE REPRESENTATION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Qing Tian, Juan Zhao, Xia Bai, Beijing Institute of Technology, China
15:20-15:30 (UTC)
23:20-23:30 (UTC +8)
17:20-17:30 (UTC +2)
08:20-08:30 (UTC -7)

TU2.R5.6: PERONA-MALIK DIFFUSION DRIVEN CNN FOR SUPERVISED CLASSIFICATION OF HYPERSPECTRAL IMAGES

Ning Wen, Qichao Liu, Liang Xiao, Nanjing University of Science and Technology, China
15:30-15:40 (UTC)
23:30-23:40 (UTC +8)
17:30-17:40 (UTC +2)
08:30-08:40 (UTC -7)

TU2.R5.7: A DIRECTIONAL MESSAGE PROPAGATION CONVOLUTIONAL NEURAL NETWORK FOR HYPERSPECTRAL IMAGES CLASSIFICATION

Jian Yu, Qichao Liu, Liang Xiao, Zhihui Wei, Nanjing University of Science and Technology, China
15:40-15:50 (UTC)
23:40-23:50 (UTC +8)
17:40-17:50 (UTC +2)
08:40-08:50 (UTC -7)

TU2.R5.8: Hyperspectral Image Classification Based on Tensor-Train Convolutional Long Short-Term Memory

Wenshuai Hu, Hengchao Li, Tianyu Ma, Southwest Jiaotong University, China; Qian Du, Mississippi State University, United States; Antonio Plaza, University of Extremadura, Spain; William J. Emery, University of Colorado, United States
15:50-16:00 (UTC)
23:50-00:00 (UTC +8)
17:50-18:00 (UTC +2)
08:50-09:00 (UTC -7)

TU2.R5.9: ADAPTIVE NEIGHBORHOOD STRATEGY BASED GENERATIVE ADVERSARIAL NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Hongbo Liang, Wenxing Bao, Bingbing Lei, Jian Zhang, Kewen Qu, School of Computer Science and Engineering, North Minzu University, China
16:00-16:10 (UTC)
00:00-00:10 (UTC +8)
18:00-18:10 (UTC +2)
09:00-09:10 (UTC -7)

TU2.R5.10: HYPERSPECTRAL IMAGE CLASSIFICATION USING SPECTRAL-SPATIAL CONVOLUTIONAL NEURAL NETWORKS

Jakub Nalepa, Lukasz Tulczyjew, Michal Myller, Michal Kawulok, KP Labs, Silesian University of Technology, Poland
16:10-16:20 (UTC)
00:10-00:20 (UTC +8)
18:10-18:20 (UTC +2)
09:10-09:20 (UTC -7)

TU2.R5.11: SEGMENTING HYPERSPECTRAL IMAGES USING SPECTRAL CONVOLUTIONAL NEURAL NETWORKS IN THE PRESENCE OF NOISE

Jakub Nalepa, Silesian University of Technology, KP Labs, Poland; Marek Stanek, Silesian University of Technology, Poland