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
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
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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Communication dans un congrès
IGARSS 2017 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2017, Houston, United States. pp.1-4, 2017, International Geoscience and Remote Sensing Symposium
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Dernière modification le : mercredi 25 octobre 2017 - 01:16:50

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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, 2017, International Geoscience and Remote Sensing Symposium. 〈hal-01620604〉

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