Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images
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 k...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/738250 |
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| author | Fenghua Huang Luming Yan |
| author_facet | Fenghua Huang Luming Yan |
| author_sort | Fenghua Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-26f6be4a1b904acea6b1db662dbb7e60 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-26f6be4a1b904acea6b1db662dbb7e602025-02-03T07:25:05ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/738250738250Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused ImagesFenghua Huang0Luming Yan1Sunshine College, Fuzhou University, Fuzhou, Fujian 350015, ChinaCollege of Geographical Sciences, Fujian Normal University, Fuzhou, Fujian 350007, ChinaTo 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.http://dx.doi.org/10.1155/2014/738250 |
| spellingShingle | Fenghua Huang Luming Yan Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images The Scientific World Journal |
| title | Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images |
| title_full | Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images |
| title_fullStr | Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images |
| title_full_unstemmed | Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images |
| title_short | Combined Kernel-Based BDT-SMO Classification of Hyperspectral Fused Images |
| title_sort | combined kernel based bdt smo classification of hyperspectral fused images |
| url | http://dx.doi.org/10.1155/2014/738250 |
| work_keys_str_mv | AT fenghuahuang combinedkernelbasedbdtsmoclassificationofhyperspectralfusedimages AT lumingyan combinedkernelbasedbdtsmoclassificationofhyperspectralfusedimages |