This paper describes a method dedicated to multi-resolution, multi-date and eventually multi-sensor classification based on explicit statistical modeling through hierarchical Markov random field modeling based on quad-Tree. The proposed approach consists in a supervised Bayesian classifier that combines a joint class-conditional statistical model for pixelwise information and a hierarchical Markov random field for spatio-Temporal and multiresolution contextual information fusion based on the Marginal Posterior Mode (MPM). The aim is to recursively maximize the posterior marginal at each pixel, which associates the most probable class label given the entire input information. Within this framework, an interesting novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with a new image in the available set in order to characterize the correlations associated with distinct images in the data set.

Nouvelle méthode en cascade pour la classification hiérarchique multi-Temporelle ou multi-capteur d'images satellitaires haute résolution

Hedhli, I.;Moser, G.;Zerubia, J.
2018-01-01

Abstract

This paper describes a method dedicated to multi-resolution, multi-date and eventually multi-sensor classification based on explicit statistical modeling through hierarchical Markov random field modeling based on quad-Tree. The proposed approach consists in a supervised Bayesian classifier that combines a joint class-conditional statistical model for pixelwise information and a hierarchical Markov random field for spatio-Temporal and multiresolution contextual information fusion based on the Marginal Posterior Mode (MPM). The aim is to recursively maximize the posterior marginal at each pixel, which associates the most probable class label given the entire input information. Within this framework, an interesting novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with a new image in the available set in order to characterize the correlations associated with distinct images in the data set.
File in questo prodotto:
File Dimensione Formato  
18.rfpt.ihsen.pdf

accesso chiuso

Tipologia: Documento in Post-print
Dimensione 3.16 MB
Formato Adobe PDF
3.16 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/928076
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact