MO2.R5.1: Training CapsNets via Active Learning for Hyperspectral Image classification
Mercedes E. Paoletti, Juan M Haut, Javier Plaza, Antonio Plaza, University of Extremadura, Spain
MO2.R5.4: HYPERSPECTRAL BAND SELECTION WITHIN A DEEP REINFORCEMENT LEARNING FRAMEWORK
Andreas Michel, Wolfgang Gross, Fabian Schenkel, Wolfgang Middelmann, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Germany
MO2.R5.5: SUPERPIXEL-LEVEL CONSTRAINT REPRESENTATION FOR HYPERSPECTRAL IMAGERY CLASSIFICATION
Haoyang Yu, Xiao Zhang, Meiping Song, Jiaochan Hu, Dalian Maritime University, China; Lianru Gao, Chinese Academy of Sciences, China
MO2.R5.6: SELF-PACED LEARNING WITH SUPERPIXELWISE FEATURES FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Xiaoxiao Tai, Guangxing Wang, Lirong Han, Xiaoyu Zhang, Peng Ren, China University of Petroleum (East China), China
MO2.R5.7: MULTISCALE CONVOLUTION NETWORK WITH REGION-BASED MAX VOTING FOR HYPERSPECTRAL IMAGES CLASSIFICATION
Xuming Zhang, Aizhu Zhang, Genyun Sun, China University of Petroleum (East China), China; Yanjuan Yao, Ministry of Environmental protection of China, China
MO2.R5.8: Improved Local Covariance Matrix Representation For Hyperspectral Image Classification
Xinyu Zhang, Yantao Wei, Huang Yao, Central China Normal University, China; Yicong Zhou, University of Macau, China
MO2.R5.10: 2D-SSA BASED MULTISCALE FEATURE FUSION FOR FEATURE EXTRACTION AND DATA CLASSIFICATION IN HYPERSPECTRAL IMAGERY
Hang Fu, Genyun Sun, China University of Petroleum (East China), China; Jinchang Ren, Jamie Zabalza, University of Strathclyde, United Kingdom; Aizhu Zhang, China University of Petroleum (East China), China; Yanjuan Yao, Ministry of Environmental protection of China, China
MO2.R5.11: MULTISCALE FEATURE EXTRACTION WITH GAUSSIAN CURVATURE FILTER FOR HYPERSPECTRAL IMAGE CLASSIFICATION
Qiaobo Hao, Shutao Li, Leyuan Fang, Xudong Kang, Hunan University, China