Hierarchical Markovian models for hyperspectral image segmentation

TL;DR

This paper introduces a hierarchical Bayesian model with hidden Markov variables for simultaneous hyperspectral image segmentation, data reduction, and spectral classification, improving upon classical methods.

Contribution

It proposes a novel hierarchical Bayesian approach that jointly addresses segmentation, classification, and data reduction in hyperspectral images using hidden Markov models.

Findings

Effective joint segmentation and classification demonstrated in simulations

Outperforms classical hyperspectral processing methods

Provides a unified framework for multiple hyperspectral analysis tasks

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.