An EM-MPM approach to unsupervised change detection in multitemporal SAR images

Liming Jiang; Mingsheng Liao; Lu Zhang; Lijun Lu; Hui Lin

doi:10.1117/12.654967

3 November 2005 An EM-MPM approach to unsupervised change detection in multitemporal SAR images

Liming Jiang, Mingsheng Liao, Lu Zhang, Lijun Lu, Hui Lin

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 60432P (2005) https://doi.org/10.1117/12.654967
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

In this paper, we propose an unsupervised change-detection method which considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images. A Markov Random Filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of independency of pixels each other and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on a MRFs 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 completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.

Citation Download Citation

Liming Jiang, Mingsheng Liao, Lu Zhang, Lijun Lu, and Hui Lin "An EM-MPM approach to unsupervised change detection in multitemporal SAR images", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60432P (3 November 2005); https://doi.org/10.1117/12.654967

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