Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Neurocomputing

Volumn 185, Issue , 2016, Pages 1-10

  Zabalza, Jaime ^a   Ren, Jinchang ^a   Zheng, Jiangbin ^b   Zhao, Huimin ^c   Qing, Chunmei ^d   Yang, Zhijing ^e   Du, Peijun ^f   Marshall, Stephen ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^b NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^c Guangdong Technical Normal Institute   (China)

^d SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^e GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^f NANJING UNIVERSITY   (China)

Author keywords

Data reduction; Deep learning (DL); Hyperspectral remote sensing; Segmented stacked autoencoder (S SAE)

Indexed keywords

ABSTRACTING; CLASSIFICATION (OF INFORMATION); EXTRACTION; FEATURE EXTRACTION; LEARNING SYSTEMS; REMOTE SENSING; SPECTROSCOPY;

AUTO ENCODERS; DATA CLASSIFICATION; DEEP LEARNING; DIMENSIONALITY REDUCTION; HIGH-LEVEL ABSTRACTION; HYPERSPECTRAL IMAGING; HYPERSPECTRAL REMOTE SENSING; REDUCED COMPLEXITY;

DATA REDUCTION;

ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; FEATURE EXTRACTION; HYPERSPECTRAL REMOTE SENSING; IMAGE PROCESSING; INTERMETHOD COMPARISON; MACHINE LEARNING; MEASUREMENT ACCURACY; PRIORITY JOURNAL; REMOTE SENSING; SEGMENTED STACKED AUTOENCODER;

EID: 84961344134     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.11.044     Document Type: Article

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