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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Nielsen F.; Barbaresco F. International Conference on Networked Geometric Science of Information, Oct 2015, Palaiseau, France. Geometric Science of Information, 9389, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-25040-3_44〉
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Gianni Franchi, Jesus Angulo. Quantization of hyperspectral image manifold using probabilistic distances. Nielsen F.; Barbaresco F. International Conference on Networked Geometric Science of Information, Oct 2015, Palaiseau, France. Geometric Science of Information, 9389, 2016, Lecture Notes in Computer Science. 〈10.1007/978-3-319-25040-3_44〉. 〈hal-01121104〉

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