2007/05/17 by Ali Mohammad‐Djafari, Ali Mohammad-Djafari, Mohammad-Djafari, Ali +4
Chemistry · Earth and Planetary Sciences · Engineering · Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Remote Sensing and Land Use #Remote-Sensing Image Classification #Spectroscopy and Chemometric Analyses #Statistics and Probability (physics.data-an) #physics.data-an
paper · pdf · doi:10.48550/arxiv.0705.2459
4 pages double column. This paper has been presented at ICPR06
arxiv created 2007/05/17 · openalex publication_date 2007/05/17 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.