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 | |
| 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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Cite this
Kasetkasem, T., & Varshney, P. K. (2002). An image change detection algorithm based on Markov random field models. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1815-1823. https://doi.org/10.1109/TGRS.2002.802498