An image change detection algorithm based on Markov random field models

Teerasit Kasetkasem, Pramod Kumar Varshney

Research output: Contribution to journalArticlepeer-review

198 Scopus citations


This paper addresses the problem of image change detection (ICD) based on Markov random field (MRF) models. MRF has long been recognized as an accurate model to describe a variety of image characteristics. Here, we use the MRF to model both noiseless images obtained from the actual scene and change images (CIs), the sites of which indicate changes between a pair of observed images. The optimum ICD algorithm under the maximum a posteriori (MAP) criterion is developed under this model. Examples are presented for illustration and performance evaluation.

Original languageEnglish (US)
Pages (from-to)1815-1823
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
StatePublished - Aug 2002


  • Change detection
  • Markov random fields
  • Maximum a posteriori (MAP) criterion
  • Multitemporal image analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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