A new change detection method based on non-parametric density estimation and Markov random fields

Guiting Wang; Yuanzhang Fan; Licheng Jiao

doi:10.1117/12.833052

30 October 2009 A new change detection method based on non-parametric density estimation and Markov random fields

Guiting Wang, Yuanzhang Fan, Licheng Jiao

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 749712 (2009) https://doi.org/10.1117/12.833052
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Guiting Wang, Yuanzhang Fan, and Licheng Jiao "A new change detection method based on non-parametric density estimation and Markov random fields", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749712 (30 October 2009); https://doi.org/10.1117/12.833052

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