Eigentargets Versus Kernel Eigentargets: Detection of Infrared Point Targets Using Linear and Nonlinear Subspace Algorithms |
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Authors: | Ruiming Liu |
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Institution: | (1) School of Electronic Engineering, Huaihai Institute of Technology, No. 59 Cangwu Road, Lianyungang, 222005, People’s Republic of China |
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Abstract: | The Eigentargets method, based on the linear principal component analysis (LPCA), has been used successfully to detect infrared
point targets. LPCA is based only on the second-order correlations without taking higher-order statistics into account. That
results in the limitation of Eigentargets in target detection. This paper extends Eigentargets, a linear subspace method,
to kernel Eigentargets, a detection method based on a nonlinear subspace algorithm. Because the kernel Eigentargets is capable
of capturing the part of higher-order statistics, the better detection performance can be achieved. Moreover, the Gaussian
intensity model is modified to generate training samples of infrared point targets. |
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Keywords: | Subspace algorithm PCA Eigentargets Kernel PCA Kernel method Kernel Eigentargets Infrared image Point target Target detection |
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