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

202 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 2002

Keywords

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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10.1109/TGRS.2002.802498

Cite this

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title = "An image change detection algorithm based on Markov random field models",

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.",

keywords = "Change detection, Markov random fields, Maximum a posteriori (MAP) criterion, Multitemporal image analysis",

author = "Teerasit Kasetkasem and Varshney, {Pramod Kumar}",

note = "Funding Information: Manuscript received July 28, 2001; revised May 27, 2002. This research was supported by the National Aeronautics and Space Administration under Grant NAG5-11227. The authors are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail: [email protected]; [email protected]). Publisher Item Identifier 10.1109/TGRS.2002.802498.",

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AU - Varshney, Pramod Kumar

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N2 - 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.

AB - 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.

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KW - Maximum a posteriori (MAP) criterion

KW - Multitemporal image analysis

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An image change detection algorithm based on Markov random field models

Abstract

Keywords

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