A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification

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

Volumn 7, Issue 1, 2014, Pages 317-326

  Kuo, Bor Chen ^a   Ho, Hsin Hua ^b   Li, Cheng Hsuan ^c   Hung, Chih Cheng ^d,e   Taur, Jin Shiuh ^b  

^a NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

^b NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

^c WUYI UNIVERSITY   (China)

^d SOUTHERN POLYTECHNIC STATE UNIVERSITY   (United States)

^e ANYANG NORMAL UNIVERSITY   (China)

Author keywords

Feature selection; hyperspectral image classification; kernel based feature selection; radial basis function; support vector machines

Indexed keywords

CLASSIFICATION PERFORMANCE; FEATURE SELECTION METHODS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE CLASSIFICATION; HYPERSPECTRAL IMAGING; LINEAR COMBINATIONS; RADIAL BASIS FUNCTION(RBF); RADIAL BASIS FUNCTIONS;

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; INDEPENDENT COMPONENT ANALYSIS; OPTIMIZATION; RADIAL BASIS FUNCTION NETWORKS; SPECTROSCOPY; SUPPORT VECTOR MACHINES; TARGET TRACKING;

IMAGE CLASSIFICATION;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGE ANALYSIS; IMAGE PROCESSING; NUMERICAL MODEL; SATELLITE DATA; SATELLITE IMAGERY;

EID: 84891831761     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2262926     Document Type: Article

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