The angular kernel in machine learning for hyperspectral data classification
International audience 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...
Published in: | 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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Main Authors: | , |
Other Authors: | , , , , , , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2010
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Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-01966042 https://hal.archives-ouvertes.fr/hal-01966042/document https://hal.archives-ouvertes.fr/hal-01966042/file/10.angular.pdf https://doi.org/10.1109/WHISPERS.2010.5594908 |
Summary: | International audience 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. |
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