Hyperspectral Image Classification with Rotation Random Forest Via KPCA

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

Volumn 10, Issue 4, 2017, Pages 1601-1609

  Xia, Junshi ^a   Falco, Nicola ^b   Benediktsson, Jon Atli ^c   Du, Peijun ^d   Chanussot, Jocelyn ^c,e  

^a UNIVERSITY OF TOKYO   (Japan)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

^c UNIVERSITY OF ICELAND   (Iceland)

^d NANJING UNIVERSITY   (China)

^e GIPSA LAB   (France)

Author keywords

Classification; hyperspectral image; kernel principal component analysis (KPCA); rotation random forest (RoRF)

Indexed keywords

DECISION TREES; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; REMOTE SENSING; SPECTROSCOPY;

ENSEMBLE APPROACHES; EXPERIMENTAL ANALYSIS; HIGH ORDER STATISTICS; HYPERSPECTRAL DATA CLASSIFICATION; HYPERSPECTRAL IMAGE CLASSIFICATION; HYPERSPECTRAL REMOTE SENSING IMAGE; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); ROTATION FORESTS;

PRINCIPAL COMPONENT ANALYSIS;

IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINE;

EID: 85008471279     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2636877     Document Type: Article

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