Nonlinear dimensionality reduction of hyperspectral images based on spectral angles and exploiting the spatial context

I proposed a nonlinear method for the dimensionality reduction of hyperspectral images is in this paper. A special feature of the proposed method is the use of spectral angles in the initial hyperspectral space as a dissimilarity measure between pixels of an image, as well as taking into account the...

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Bibliographic Details
Published in:Journal of physics. Conference series 2018-09, Vol.1096 (1), p.12037
Main Author: Myasnikov, E V
Format: Article
Language:English
Subjects:
Context
Hyperspectral imaging
Pixels
Principal components analysis
Reduction
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Summary:I proposed a nonlinear method for the dimensionality reduction of hyperspectral images is in this paper. A special feature of the proposed method is the use of spectral angles in the initial hyperspectral space as a dissimilarity measure between pixels of an image, as well as taking into account the spatial context of the hyperspectral image pixels. I used a well-known hyperspectral image dataset in the experiments. The experiments showed the advantage of the developed method over the basic nonlinear dimensionality reduction methods and the linear principal component analysis technique.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1096/1/012037