An image change detection algorithm based on Markov random field models

Teerasit Kasetkasem; Pramod Kumar Varshney

doi:10.1109/TGRS.2002.802498

An image change detection algorithm based on Markov random field models

Teerasit Kasetkasem, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

167 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	1815-1823
Number of pages	9
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	40
Issue number	8
DOIs	https://doi.org/10.1109/TGRS.2002.802498
State	Published - Aug 1 2002

Keywords

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

ASJC Scopus subject areas

Electrical and Electronic Engineering
Earth and Planetary Sciences(all)

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An image change detection algorithm based on Markov random field models

Abstract

Keywords

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