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Communication Dans Un Congrès Année : 2010

The angular kernel in machine learning for hyperspectral data classification

Cédric Richard

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.
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Dates et versions

hal-01966042 , version 1 (27-12-2018)

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Paul Honeine, Cédric Richard. The angular kernel in machine learning for hyperspectral data classification. Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2010, Reykjavik, Iceland. pp.1-4, ⟨10.1109/WHISPERS.2010.5594908⟩. ⟨hal-01966042⟩
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