IEEE IGARSS 2020 || Virtual Symposium || September 26 - October 2, 2020

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

Session:

Hyperspectral Image Classification I

Track:

Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)

Presentation Time:

Mon, 28 Sep, 16:00-16:10 (UTC)
Tue, 29 Sep, 00:00-00:10 China Standard Time (UTC +8)
Mon, 28 Sep, 18:00-18:10 Central Europe Summer Time (UTC +2)
Mon, 28 Sep, 09:00-09:10 Pacific Daylight Time (UTC -7)

Session Co-Chairs:

Antonio Plaza, University of Extremadura and Fabio Pacifici, Maxar

Session Managers:

Vanessa Núñez and Shivam Pande

Presentation

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Session

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.R5.2: DIMENSIONALITY REDUCTION WITH WEIGHTED K-MEANS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Michael Wong, Chih-Cheng Hung, Kennesaw State University, United States

MO2.R5.3: STATISTICAL PERSPECTIVE OF SOM AND CSOM FOR HYPER-SPECTRAL IMAGE CLASSIFICATION

Srivatsa Mallapragada, Chih-Cheng Hung, Kennesaw State University, United States

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.R5.9: HYPERSPECTRAL IMAGE CLASSIFICATION VIA OBJECT-ORIENTED SEGMENTATION-BASED SEQUENTIAL FEATURE EXTRACTION AND RECURRENT NEURAL NETWORK

Andong Ma, Anthony M. Filippi, Texas A&M University, United States

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