Hierarchical Markovian Models for Hyperspectral Image Segmentation

Abstract

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.

Invariant representation for spectral reflectance images and its application

“…Several algorithms have been proposed for segmenting the spectral images into distinct surface areas [15][16][17][18][19]. Paclík et al [15] proposed a segmentation method of spectral images acquired by imaging cross-sections with scanning electron microscopy using energy-dispersive X-ray microanalysis (SEM/EDX).…”

“…An effective method was introduced by Martínez-Usó et al [16] for spectral image segmentation in fruit inspection applications. A hierarchical Markovian model for hyperspectral image segmentation was proposed by Mohammad-Djafari et al [17]. However, those algorithms were not always robust for the highlight and shading effects occurred for different surface materials, and required a careful adjustment of the lighting position.…”

“…Several algorithms were recently proposed for segmenting the spectral images into distinct surface areas [15][16][17][18][19]. However, those algorithms were not always robust for highlight and shading effects occurred for different surface materials, and required a careful adjustment of the lighting position.…”