An image fusion approach based on markov random fields

Min Xu, Hao Chen, Pramod K. Varshney

Research output: Contribution to journalArticle

70 Scopus citations

Abstract

Markov random field (MRF) models are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. This paper investigates the problem of fusion of remote sensing images, e.g., multispectral image fusion, based on MRF models and incorporates the contextual constraints via MRF models into the fusion model. Fusion algorithms under the maximum a posteriori criterion are developed to search for solutions. Our algorithm is applicable to both multiscale decomposition (MD)-based image fusion and non-MD-based image fusion. Experimental results are provided to demonstrate the improvement of fusion performance by our algorithms.

Original languageEnglish (US)
Article number5963713
Pages (from-to)5116-5127
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume49
Issue number12 PART 2
DOIs
StatePublished - Dec 1 2011

Keywords

  • Markov random field
  • multi-resolution decomposition
  • multispectral image fusion

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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