Kernel Spectral Matched Filter for Hyperspectral Imagery
Kernel Spectral Matched Filter for Hyperspectral Imagery
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DOI：
10.1007/s11263-006-6689-3
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发表时间：
2007-02
影响因子：
19.5
通讯作者：
H. Kwon;N. Nasrabadi
中科院分区：
文献类型：
--
作者：
H. Kwon;N. Nasrabadi
本文提出了一种基于核学习理论的非线性光谱匹配滤波器用于高光谱图像目标检测。一个光谱匹配滤波器被定义在一个高维的特征空间，这是隐式生成的非线性映射与核函数。一个内核版本的匹配滤波器是通过表达的频谱匹配滤波器的矢量点积形式和取代每个点积与核函数使用所谓的内核trickproperty的themercernels。提出的核谱匹配滤波器等价于原始输入空间中的非线性匹配滤波器，能够产生非线性判决边界。实现了线性光谱匹配滤波器的核版本，对高光谱图像的仿真结果表明，核光谱匹配滤波器优于传统的线性匹配滤波器。
In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so calledkernel trickproperty of theMercerkernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.