Incorporating global-local a Priori knowledge into expectation-maximization for SAR image change detection

ABSTRACT

Speckle is one of the inevitable obstacles related to synthetic aperture radar (SAR) image change detection; it increases the overlap between changed and unchanged pixels in the histogram of a difference image. This makes the selection of a statistic model more difficult for describing opposite classes. To address this issue, this article developed an unsupervised change-detection approach for multitemporal SAR images that specifies a priori knowledge about the spatial characteristics of the classes through Dempster-Shafer evidence theory and embeds it into the Expectation-Maximization (EM) iteration process. It is based on the consideration that each pixel in the difference image is unique due to its neighbourhood, although some of them may have the same pixel value. Thus, under the hypothesis that local and global a priori knowledge are independent sources, a global-local a priori model is developed through Dempster-Shafer evidence theory. The EM algorithm allows one to estimate the statistical parameters of the opposite classes associated with this a priori model. As a consequence, the change-detection result can be obtained within the framework of Bayes. Visual and quantitative results obtained on real multitemporal SAR image data sets confirm the effectiveness of the proposed method compared with state-of-the-art ones for SAR image change detection.

Acknowledgments

The authors would like to thank the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of Xidian University for help in providing the test data sets. This work was supported by the National Natural Science Foundation of China (Grant No. 61702158 and No. 61703332).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [61806163, 61702158, 61703332].