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Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images

Bharath Damodaran 1 Nicolas Courty 1 Romain Tavenard 1
1 OBELIX - Environment observation with complex imagery
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, UBS - Université de Bretagne Sud
Abstract : Kernel based feature extraction method overcomes the curse of dimensionality and captures the non-linearities present in the data. However, these methods are not scal-able with large number of pixels found with hyperspec-tral images. Thus, a small subset of pixels are randomly selected to make the solution of kernel based methods tractable. In this paper, we propose scalable nonlinear component analysis for dimensionality reduction of hy-perspectral images. The proposed method relies on the randomized feature maps to capture the non-linearities between the variables in the hyperspectral data. Experiments conducted with three hyperspectral datasets show that our proposed method has provided better quality components and outperformed the state-of-the-art in terms of classification performance.
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Submitted on : Friday, October 20, 2017 - 5:26:10 PM
Last modification on : Wednesday, November 3, 2021 - 6:05:37 AM
Long-term archiving on: : Sunday, January 21, 2018 - 1:45:02 PM

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  • HAL Id : hal-01620604, version 1

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Bharath Damodaran, Nicolas Courty, Romain Tavenard. Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images. IGARSS 2017 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2017, Houston, United States. pp.1-4. ⟨hal-01620604⟩

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