The angular kernel in machine learning for hyperspectral data classification
Résumé
Support vector machines have been investigated with success for hyperspectral data classification. In this paper, we propose a new kernel to measure spectral similarity, called the angular kernel. We provide some of its properties, such as its invariance to illumination energy, as well as connection to previous work. Furthermore, we show that the performance of a classifier associated to the angular kernel is comparable to the Gaussian kernel, in the sense of universality. We derive a class of kernels based on the angular kernel, and study the performance on an urban classification task.
Mots clés
data handling
Gaussian processes
geophysical image processing
image classification
learning (artificial intelligence)
angular kernel
hyperspectral data classification
illumination energy
Gaussian kernel
urban classification task
hyperspectral images
Kernel
Support vector machines
Hyperspectral imaging
Machine learning
Spatial resolution
Hyperspectral data
spectral angle
SVM
reproducing kernel
| Origine | Fichiers produits par l'(les) auteur(s) |
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