Bayesian segmentation of hyperspectral images
| dc.creator | Mohammadpour, Adel | |
| dc.creator | Féron, Olivier | |
| dc.creator | Mohammad-Djafari, Ali | |
| dc.date | 2007-08-22 | |
| dc.date.accessioned | 2026-07-07T08:24:58Z | |
| dc.date.available | 2026-07-07T08:24:58Z | |
| dc.description | In this paper we consider the problem of joint segmentation of hyperspectral images in the Bayesian framework. The proposed approach is based on a Hidden Markov Modeling (HMM) of the images with common segmentation, or equivalently with common hidden classification label variables which is modeled by a Potts Markov Random Field. We introduce an appropriate Markov Chain Monte Carlo (MCMC) algorithm to implement the method and show some simulation results. | |
| dc.description | 8 pages, 2 figures, presented at MaxEnt 2004, Inst. Max Planck, Garching, Germany | |
| dc.identifier | https://arxiv.org/abs/0708.3013 | |
| dc.identifier | http://arxiv.org/abs/0708.3013 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/136520 | |
| dc.subject | Data Analysis, Statistics and Probability | |
| dc.title | Bayesian segmentation of hyperspectral images | |
| dc.type | text |