IEEE IGARSS 2021 || Brussels, Belgium || July 12–14, 2021 • Physical

TU1.O-2.4

LSTM-ADVERSARIAL AUTOENCODER FOR SPECTRAL FEATURE LEARNING IN HYPERSPECTRAL ANOMALY DETECTION

Tongbin Ouyang, Jinshen Wang, Xinyue Zhao, Shujie Wu, Beihang University, China

Session:

Deep Learning Based Feature Extraction

Track:

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

Presentation Time:

Tue, 13 Jul, 09:15-09:30 (UTC)
Tue, 13 Jul, 11:15-11:30 Central Europe Summer Time (UTC +2)
Tue, 13 Jul, 17:15-17:30 China Standard Time (UTC +8)
Tue, 13 Jul, 05:15-05:30 Eastern Daylight Time (UTC -4)

Session Co-Chairs:

Liangpei Zhang, Wuhan University and Luis Gòmez-Chova, Universidad de València

Session Manager:

Alexandru Neculai

Session TU1.O-2

TU1.O-2.1: GENERALIZED SCALABLE NEIGHBORHOOD COMPONENT ANALYSIS FOR SINGLE AND MULTI-LABEL REMOTE SENSING IMAGE CHARACTERIZATION

Jian Kang, School of Electronic and Information Engineering, Soochow University, China; Ruben Fernandez-Beltran, Institute of New Imaging Technologies, University Jaume I, Spain; Antonio Plaza, Hyperspectral Computing Laboratory, University of Extremadura, Spain

TU1.O-2.2: MOATNET: REGISTRATION FOR MULTI-TEMPORAL OPTICAL REMOTE SENSING IMAGES USING DEEP CONVOLUTIONAL FEATURES

Chao Li, Yanan You, Jingyi Cao, Wenli Zhou, Beijing University of Posts and Telecommunications, China

TU1.O-2.3: HYPERSPECTRAL IMAGE DENOISING BASED ON MULTI-STREAM DENOISING NETWORK

Yan Gao, Feng Gao, Junyu Dong, Ocean University of China, China

TU1.O-2.4: LSTM-ADVERSARIAL AUTOENCODER FOR SPECTRAL FEATURE LEARNING IN HYPERSPECTRAL ANOMALY DETECTION

Tongbin Ouyang, Jinshen Wang, Xinyue Zhao, Shujie Wu, Beihang University, China

TU1.O-2.5: GRAPH REGULARIZED AUTOENCODER BASED FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Xiaotian Fan, Jingzhou Chen, Yuntao Qian, Zhejiang University, China

TU1.O-2.6: HYPERSPECTRAL IMAGE SUPER-RESOLUTION BASED ON MULTISCALE RESIDUAL BLOCK AND MULTILEVEL FEATURE FUSION

Gang Yu, Feng Zhang, Ting Hu, Wei Li, Ran Tao, Beijing Institute of Technology, China

LSTM-ADVERSARIAL AUTOENCODER FOR SPECTRAL FEATURE LEARNING IN HYPERSPECTRAL ANOMALY DETECTION

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