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An image change detection algorithm based on Markov random field models
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DOI：10.1109/TGRS.2002.802498.png)
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Abstract
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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
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