Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm

Abstract

Change detection in synthetic aperture radar (SAR) images has become increasingly important along with the development of SAR techniques. In this study, a novel locally fitting and expectation-maximization (EM) approach is proposed for unsupervised change detection tasks in SAR images. A difference image is generated first, and an in-depth study of the inherent characteristics of the histogram of the difference image is then made. Thus, the new approach is proposed corresponding to these characteristics. In this approach, the locally fitting model orientated to deal with the unchanged class is put forward to reach a best fit, and the semi-EM algorithm for the changed class is used to tackle the phenomenon of overlapping. Then, through the Bayesian decision rule, the optimal threshold is determined. Our contributions lie in two aspects. First, in the locally fitting model, the location of the optimal threshold can be determined, which leads to an accurate fit over a short interval. Second, the use of the semi-EM algorithm not only retains the efficacy of the EM algorithm to cope with overlapping for the changed class but also simplifies the computing process. Experiments on real data sets confirm the effectiveness of the proposed approach, which results in final maps very similar to the ground truth and is more effective in determining the optimal threshold in comparison with others. The experimental results also demonstrate its effectiveness when the changed areas are of different geometrical shapes.

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 61273317]; the National Top Youth Talents Support Programme of China, and the Fundamental Research Fund for the Central Universities [Grant Nos K50510020001 and K5051202053].