Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18758
Title: A multiple-mapping kernel for hyperspectral image classification
Authors: Wang, L
Hao, S
Wang, Q
Atkinson, PM
Keywords: Hyperspectral image classification
Multiplemapping kernel
Support vector machine (SVM)
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE geoscience and remote sensing letters, 2015, v. 12, no. 5, 6982214, p. 978-982 How to cite?
Journal: IEEE geoscience and remote sensing letters 
Abstract: The kernel function plays an important role in machine learning methods such as the support vector machine. In this letter, a new kernel framework is developed for hyperspectral image classification. In contrast to existing composite kernels constructed via a linearly weighted combination, the multiple-mapping kernel proposed in this letter is obtained through repeated nonlinear mappings. Experiments indicate that the proposed multiple-mapping kernel framework (MMKF) is effective for hyperspectral image classification. Compared to the single kernel methods, the MMKF tends to be more advantageous in terms of classification accuracy, particularly for the situation with a small-size training set.
URI: http://hdl.handle.net/10397/18758
ISSN: 1545-598X
DOI: 10.1109/LGRS.2014.2371044
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