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Hyperspectral Imaging Target Detection Based on Improved Kernel Principal Component Analysis


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Abstract

The kernel principal component analysis (KPCA) algorithm has been extensively used in target detection and classification for hyperspectral imaging. Kernel parameters are a key component of KPCA, but no optimal method for selecting appropriate parameters has been proposed. We study the largest eigenvalue and sum of the eigenvalues of characteristic equation of kernel matrix, and then put forward a proposal for parameter selection. Experiments show that the proposal enables best parameter selection.


Keywords


Pages

Total Pages: 12
Pages: 873-884

DOI
10.1080/10798587.2012.10643295


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Published

Volume: 18
Issue: 7
Year: 2012

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References

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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