title: Combined kernel-based BDT-SMO classification of hyperspectral fused images.
abstract: 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.
authors.affiliation: Sunshine College, Fuzhou University, Fuzhou, Fujian 350015, China ; College of Geographical Sciences, Fujian Normal University, Fuzhou, Fujian 350007, China.
authors.forename: Fenghua
authors.initials: F
authors.lastname: Huang
authors.affiliation: College of Geographical Sciences, Fujian Normal University, Fuzhou, Fujian 350007, China.
authors.forename: Luming
authors.initials: L
authors.lastname: Yan
mesh_terms: D001185:Artificial Intelligence / Q000145:classification*; D003663:Decision Trees; D010363:Pattern Recognition, Automated / Q000145:classification* / Q000379:methods; D060388:Support Vector Machine