Hierarchical markovian models for joint classification, segmentation and data reduction of hyperspectral images

Spectral classification, segmentation and data reduction are the three main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach which tries to give a solution for these three problems jointly. The data reduction problem is modeled as a blind sources separation (BSS) where the data are the m hyperspectral images and the sources are the n < m images which must be mutually the most independent and piecewise homogeneous. To insure these properties, we propose a hierarchical model for the sources with a common hidden classification variable which is modelled via a Potts-Markov field. The joint Bayesian estimation of this hidden variable as well as the sources and the mixing matrix of the BSS problem gives a solution for all the three problems of spectra classification, segmentation and data reduction problems of hyperspectral images. An appropriate Gibbs Sampling (GS) algorithm is proposed for the Bayesian computationand a few simulation resu...

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Nadia Bali, Ali Mohammad-Djafari, Adel Mohammadpour
• Keywords: Data Reduction, Data Reduction Problems, ESANN 2006, ESANN 2007, Hyperspectral Images