Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 56, Issue 1, 2018, Pages 391-406

  Mou, Lichao ^a,b   Ghamisi, Pedram ^a,b   Zhu, Xiao Xiang ^a,b  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Convolutional network; Deconvolutional network; Hyperspectral image classification; Residual learning; Unsupervised spectral spatial feature learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); CONVOLUTION; DECODING; FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; OBJECT DETECTION; PERSONNEL TRAINING; REMOTE SENSING; SEMANTICS; SIGNAL ENCODING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

COMPETITIVE PERFORMANCE; CONVOLUTIONAL NETWORKS; DIMENSIONAL SPACES; EXPERIMENTAL ANALYSIS; REPRESENTATIVE SAMPLE; RESIDUAL LEARNING; SPATIAL FEATURES; UNSUPERVISED FEATURE LEARNING;

NETWORK ARCHITECTURE;

EID: 85032438871     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2748160     Document Type: Article

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