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Proceedings Paper

A new change detection method based on non-parametric density estimation and Markov random fields
Author(s): Guiting Wang; Yuanzhang Fan; Licheng Jiao
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Paper Abstract

A new change detection approach based on non-parametric density estimation and Markov random fields is proposed in this paper. As the concrete form of gray statistical distribution of remote sensing images is often difficult to be known, the non-parameter density estimation method does not need the specific forms in advance, and is especially suitable for the estimation problem of small samples, so we adopt the non-parametric density estimation method to obtain the precise estimation of the probability density of statistical distribution of differencing image in the paper, and then perform multitemporal remote sensing image change detection combining with MRF(Markov random fields)model for spatial smoothing. The final experimental results show that the proposed method is effective.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749712 (30 October 2009); doi: 10.1117/12.833052
Show Author Affiliations
Guiting Wang, Xidian Univ. (China)
Key Lab. of Intelligent Perception and Image Understanding (China)
Yuanzhang Fan, Xidian Univ. (China)
Key Lab. of Intelligent Perception and Image Understanding (China)
Licheng Jiao, Xidian Univ. (China)
Key Lab. of Intelligent Perception and Image Understanding (China)


Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)

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