Ideal regularized kernel for hyperspectral image classification
Peng, Jiangtao1; Zhou, Yicong2
Conference Name36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Source PublicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
Conference Date7 10, 2016 - 7 15, 2016
Conference PlaceBeijing, China
Author of SourceInstitute of Electrical and Electronics Engineers Inc.
AbstractThis 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. © 2016 IEEE.
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Hubei University, Faculty of Mathematics and Statistics, China;
2.University of Macau, Faculty of Science and Technology, China
Recommended Citation
GB/T 7714
Peng, Jiangtao,Zhou, Yicong. Ideal regularized kernel for hyperspectral image classification[C]//Institute of Electrical and Electronics Engineers Inc.,2017:3274-3277.
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