| Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models |
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| 作者姓名： | JIANG Liming LIAO Mingsheng ZHANG Lu LIN Hui |
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| 作者单位： | JIANG Liming LIAO Mingsheng ZHANG Lu LIN Hui JIANG Liming，Institute of Space and Earth Information Science，The Chinese University of Hong Kong，Shatin，N.T.，Hong Kong，China. |
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| 摘 要： | An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.
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| 关 键 词： | 多时相遥感图象 SAR图象 MRF模型 无监督变化检测 |
| 文章编号： | 1009-5020（2007）02-111-06 |
| 收稿时间： | 2 April 2007 |
| 修稿时间： | 2007-04-02 |
Unsupervised change detection in multitemporal SAR images using MRF models |
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| JIANG Liming LIAO Mingsheng ZHANG Lu LIN Hui.Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models[J].Geo-Spatial Information Science,2007,10(2):111-116. |
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| Authors: | Jiang Liming Liao Mingsheng Zhang Lu Lin Hui |
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| Institution: | (1) Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China |
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| Abstract: | An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method. |
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| Keywords: | change detection multitemporal SAR image Markov random field EM algorithm |
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