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Ideal Regularized Kernels for Hyperspectral Image Classification
Jiangtao Peng1; Yicong Zhou2
Conference Name2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Source Publication2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference Date10-15 July 2016
Conference PlaceBeijing, China

This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral images (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of ideal kernel according to a regularization kernel learning framework, which captures both the sample similarity and label similarity and makes the resulting kernel more appropriate for HSI classification tasks. With the ideal regularization, the kernel learning problem has a simple analytical solution and is very easy to implement. The ideal regularization can be used to improve and refine state-of-the-art kernels, including spectral kernels, spatial kernels and spectral-spatial composite kernels. The effectiveness of the proposed IRCK is validated on the benchmark hyperspectral data set: Indian Pines. Experimental results show the superiority of our ideal regularized composite kernel method over the classical kernel methods.

KeywordHyperspectral Image Classification Ideal Kernel Regularization Composite Kernel
Indexed BySCI
WOS Research AreaEngineering ; Geology ; Remote Sensing
WOS SubjectEngineering, Electrical & Electronic ; Geosciences, Multidisciplinary ; Remote Sensing
WOS IDWOS:000388114603075
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.Hubei University Faculty of Mathematics and Statistics
2.University of Macau Faculty of Science and Technology
Recommended Citation
GB/T 7714
Jiangtao Peng,Yicong Zhou. Ideal Regularized Kernels for Hyperspectral Image Classification[C],2016.
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