Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Randomized Nonlinear Component Analysis for Dimensionality Reduction of Hyperspectral Images

Résumé

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.
Fichier principal
Vignette du fichier
RNLCA.pdf (2.08 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01620604 , version 1 (20-10-2017)

Identifiants

  • HAL Id : hal-01620604 , version 1

Citer

Bharath Bhushan 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⟩
616 Consultations
366 Téléchargements

Partager

Gmail Facebook X LinkedIn More