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Quantization of Hyperspectral Image Manifold Using Probabilistic Distances

  • Gianni Franchi
  • Jesús AnguloEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9389)

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 \(\alpha \)-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.

Keywords

Quantization Hyperspectral images Information geometry Probabilistic distances Mathematical morphology 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.MINES ParisTech, CMM-Centre de Morphologie MathématiquePSL-Research UniversityParisFrance

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