Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-27
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
To solve the poor generalization and flexibility problems that single kernel SVM classifiers have while classifying combined spectral and spatial features, this paper proposed a solution to improve the classification accuracy and efficiency of hyperspectral fused images: (1) different radial basis kernel functions (RBFs) are employed for spectral and textural features, and a new combined radial basis kernel function (CRBF) is proposed by combining them in a weighted manner; (2) the binary decision tree-based multiclass SMO (BDT-SMO) is used in the classification of hyperspectral fused images; (3) experiments are carried out, where the single radial basis function- (SRBF-) based BDT-SMO classifier and the CRBF-based BDT-SMO classifier are used, respectively, to classify the land usages of hyperspectral fused images, and genetic algorithms (GA) are used to optimize the kernel parameters of the classifiers.
The results show that, compared with SRBF, CRBF-based BDT-SMO classifiers display greater classification accuracy and efficiency.
American Psychological Association (APA)
Huang, Fenghua& Yan, Luming. 2014. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1050842
Modern Language Association (MLA)
Huang, Fenghua& Yan, Luming. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images. The Scientific World Journal No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1050842
American Medical Association (AMA)
Huang, Fenghua& Yan, Luming. Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1050842
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050842