TY - JOUR
T1 - An image change detection algorithm based on Markov random field models
AU - Kasetkasem, Teerasit
AU - Varshney, Pramod Kumar
N1 - 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: tkasetka@syr.edu; varshney@syr.edu). Publisher Item Identifier 10.1109/TGRS.2002.802498.
PY - 2002/8
Y1 - 2002/8
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.
KW - Change detection
KW - Markov random fields
KW - Maximum a posteriori (MAP) criterion
KW - Multitemporal image analysis
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U2 - 10.1109/TGRS.2002.802498
DO - 10.1109/TGRS.2002.802498
M3 - Article
AN - SCOPUS:0036699475
VL - 40
SP - 1815
EP - 1823
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
SN - 0196-2892
IS - 8
ER -