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This œuvre, Quantization of hyperspectral image manifold using probabilistic distances, by Jesus Angulo is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license.

Quantization of hyperspectral image manifold using probabilistic distances


Quantization of hyperspectral image manifold using probabilistic distances
Publication details: 
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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Quantization of hyperspectral image manifold using probabilistic distances
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