Combining ensemble technique of support vector machines with the optimal kernel method for hyperspectral image classification

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn , Issue , 2011, Pages 3903-3906

  Kuo, Bor Chen ^a   Chen, I Ling ^a   Li, Cheng Hsuan ^a,b   Hung, Chih Cheng ^c  

^a NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^c SOUTHERN POLYTECHNIC STATE UNIVERSITY   (United States)

Author keywords

classification; ensemble; kernel function; subspace method; SVM

Indexed keywords

ADAPTIVE SUBSPACES; COMPONENT CLASSIFIERS; COMPONENT DIMENSIONS; CURSE OF DIMENSIONALITY; ENSEMBLE; ENSEMBLE TECHNIQUES; HIGH DIMENSIONALITY; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE CLASSIFICATION; KERNEL FUNCTION; KERNEL METHODS; MULTIPLE CLASSIFIER SYSTEMS; RANDOM SUBSPACE METHOD; RESUBSTITUTION; SMALL SAMPLE SIZE; SUB-SPACE METHODS; SUBSPACE SELECTION; SVM; WASHINGTON;

GEOLOGY; INDEPENDENT COMPONENT ANALYSIS; OPTIMIZATION; PATTERN RECOGNITION SYSTEMS; REMOTE SENSING; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 80955170645     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2011.6050084     Document Type: Conference Paper

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