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Hyperspectral Target Detection Using Kernel Spectral Matched Filter

Heesung Kwon, Nasser M. Nasrabadi
CVPR 2004 pp. 127
doi:10.1109/CVPR.2004.363 /cvpr/2004/kwon2004cvpr-hyperspectral/

Abstract

In this paper a non-linear matched filter is introduced for target detection in hyperspectral imagery which is implemented by using the ideas in kernel-based learning theory. The proposed non-linear matched filter exploits the notion that performing matched filtering in a non-linear feature space of high dimensionality increases the probability of detection. Defining matched filter in a kernel feature space is equivalent to a non-linear matched filter in the original input space which allows the higher order correlation between the spectral bands to be exploited. It is also shown that the non-linear matched filter can easily be implemented using the ideas of kernel functions. The kernel version of the non-linear matched filter is implemented and simulation results are shown to outperform the linear version.

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Cite

Text

Kwon and Nasrabadi. "Hyperspectral Target Detection Using Kernel Spectral Matched Filter." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.363

Markdown

[Kwon and Nasrabadi. "Hyperspectral Target Detection Using Kernel Spectral Matched Filter." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/kwon2004cvpr-hyperspectral/) doi:10.1109/CVPR.2004.363

BibTeX

@inproceedings{kwon2004cvpr-hyperspectral,
  title     = {{Hyperspectral Target Detection Using Kernel Spectral Matched Filter}},
  author    = {Kwon, Heesung and Nasrabadi, Nasser M.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {127},
  doi       = {10.1109/CVPR.2004.363},
  url       = {https://mlanthology.org/cvpr/2004/kwon2004cvpr-hyperspectral/}
}