ML Anthology

A Machine Learning Approach for Material Detection in Hyperspectral Images

Raphaël Marée, Benjamin Stevens, Pierre Geurts, Y. Guern, P. Mack
CVPRW 2009 pp. 106-111
doi:10.1109/CVPRW.2009.5204119 /cvprw/2009/maree2009cvprw-machine/

Abstract

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes and by using extremely randomized trees with multiple outputs as a classifier. Promising results are shown on a dataset of more than 60 hypercubes.

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Cite

Text

Marée et al. "A Machine Learning Approach for Material Detection in Hyperspectral Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009. doi:10.1109/CVPRW.2009.5204119

Markdown

[Marée et al. "A Machine Learning Approach for Material Detection in Hyperspectral Images." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2009.](https://mlanthology.org/cvprw/2009/maree2009cvprw-machine/) doi:10.1109/CVPRW.2009.5204119

BibTeX

@inproceedings{maree2009cvprw-machine,
  title     = {{A Machine Learning Approach for Material Detection in Hyperspectral Images}},
  author    = {Marée, Raphaël and Stevens, Benjamin and Geurts, Pierre and Guern, Y. and Mack, P.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2009},
  pages     = {106-111},
  doi       = {10.1109/CVPRW.2009.5204119},
  url       = {https://mlanthology.org/cvprw/2009/maree2009cvprw-machine/}
}