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31 January 2020 Dimensionality reduction of hyperspectral images based on the linear mixture model and dimensionality estimation
Evgeny Myasnikov
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114331L (2020) https://doi.org/10.1117/12.2559412
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
In this paper, we propose a dimensionality reduction technique, which is based on the principal component analysis of homogenous spatial regions of hyperspectral images. In the proposed technique, we rely on the linear mixture model and use a dimensionality estimation procedure to split an image into homogenous regions. The experiments carried out using well-known hyperspectral image scenes show that the proposed technique allows obtaining compact representations of image regions in reduced spectral subspaces and can be considered as a segmentation technique.
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Evgeny Myasnikov "Dimensionality reduction of hyperspectral images based on the linear mixture model and dimensionality estimation", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114331L (31 January 2020); https://doi.org/10.1117/12.2559412
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KEYWORDS
Hyperspectral imaging
Image segmentation
Principal component analysis
Image classification
Image analysis
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