Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 12, Issue 7, 2019, Pages 2159-2169

  Feng, Wei ^a   Dauphin, Gabriel ^b   Huang, Wenjiang ^a   Quan, Yinghui ^c   Bao, Wenxing ^d   Wu, Mingquan ^a   Li, Qiang ^e  

^a Chinese Academy of Sciences   (China)

^b UNIVERSITÉ PARIS 13   (France)

^c XIDIAN UNIVERSITY   (China)

^d BEIFANG UNIVERSITY OF NATIONALITIES   (China)

^e INSTITUTE OF THEORETICAL PHYSICS   (China)

Author keywords

Ensemble learning; hyperspectral image classification; imbalance learning; rotation forest (RoF)

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; HYPERSPECTRAL IMAGING; ITERATIVE METHODS; REMOTE SENSING; SPECTROSCOPY;

CLASS IMBALANCE LEARNING; CLASS IMBALANCE PROBLEMS; ENSEMBLE CLASSIFIERS; ENSEMBLE LEARNING; HYPERSPECTRAL DATA CLASSIFICATION; IMBALANCE LEARNING; ROTATION FORESTS; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES;

ROTATION;

ACCURACY ASSESSMENT; ALGORITHM; DATA SET; FOREST DYNAMICS; IMAGE CLASSIFICATION; MACHINE LEARNING; REMOTE SENSING; SATELLITE IMAGERY; SPECTRAL ANALYSIS;

EID: 85070566454     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2019.2922297     Document Type: Article

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