Hierarchical Markovian Models for Hyperspectral Image Segmentation

  • Ali Mohammad-Djafari
  • Nadia Bali
  • Adel Mohammadpour
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4153)


Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral classification and image segmentation. In the proposed model, the desired independent components are piecewise homogeneous images which share the same common hidden segmentation variable. Thus, the joint Bayesian estimation of this hidden variable as well as the sources and the mixing matrix of the source separation problem gives a solution for all the three problems of dimensionality reduction, spectra classification and segmentation of hyperspectral images. A few simulation results illustrate the performances of the proposed method compared to other classical methods usually used in hyperspectral image processing.


Independent Component Analysis Hyperspectral Image Hide Variable Independent Component Analysis Mean Field Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sasaki, K., Kawata, S., Minami, S.: Component analysis of spatial and spectral patterns in multispectral images. I. basics. Journal of the Optical Society of America. A 4(11), 2101–2106 (1987)CrossRefGoogle Scholar
  2. 2.
    Parra, L., Spence, C., Ziehe, A., Mueller, K.-R., Sajda, P.: Unmixing hyperspectral data. In: Advances in Neural Information Processing Systems 13 (NIPS 2000), pp. 848–854. MIT Press, Cambridge (2000)Google Scholar
  3. 3.
    Bali, N., Mohammad-Djafari, A.: Mean Field Approximation for BSS of images with compound hierarchical Gauss-Markov-Potts model. In: MaxEnt 2005, San José CA,US, American Institute of Physics (AIP) (August 2005)Google Scholar
  4. 4.
    Snoussi, H., Mohammad-Djafari, A.: Fast joint separation and segmentation of mixed images. Journal of Electronic Imaging 13(2), 349–361 (2004)CrossRefGoogle Scholar
  5. 5.
    Zhang, J.: The mean field theory in EM procedures for blind Markov random field image restoration. Trans. Image Processing 2(1), 27–40 (1993)CrossRefGoogle Scholar
  6. 6.
    Landgrebe, D.: Hyperspectral image data analysis. Trans. Signal Processing 19, 17–28 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ali Mohammad-Djafari
    • 1
  • Nadia Bali
    • 1
  • Adel Mohammadpour
    • 1
  1. 1.Laboratoire des Signaux et Systèmes, Unité mixte de recherche 8506 (CNRS-Supélec-UPS)Gif-sur-YvetteFrance

Personalised recommendations