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Conference Papers Year : 2016

Quantization of hyperspectral image manifold using probabilistic distances

Abstract

A technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on α−connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit the hyperspectral data manifold, a comparison of different probabilistic dissimilarity measures is achieved.
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Dates and versions

hal-01121104 , version 1 (27-02-2015)

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Gianni Franchi, Jesus Angulo. Quantization of hyperspectral image manifold using probabilistic distances. International Conference on Networked Geometric Science of Information, Oct 2015, Palaiseau, France. ⟨10.1007/978-3-319-25040-3_44⟩. ⟨hal-01121104⟩
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