Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation

ABSTRACT

In this article, we propose a novel unsupervised change detection method for synthetic aperture radar (SAR) images. First, we generate a difference image as a weighted average of a log-ratio image and a mean-ratio image, which has the advantage of enhancing the information of changed regions and restraining the information of unchanged background regions simultaneously. Second, we propose a variational soft segmentation model based on non-differentiable curvelet regularization and L1-norm fidelity. Numerically, by using the split Bregman technique for curvelet regularization term and reformulating the L1-norm fidelity as weighted L2-norm fidelity, we get an effective algorithm in which each sub-problem has a closed-form solution. The numerical experiments and comparisons with several existing methods show that the proposed method is promising, with not only high robustness to non-Gaussian noise or outliers but also high change detection accuracy. Moreover, the proposed method is good at detecting fine-structured change areas. Especially, it outperforms other methods in preserving edge continuity and detecting curve-shaped changed areas.

Acknowledgments

This work is supported by the 973 Program (2011CB707104), the Science and Technology Commission of Shanghai Municipality (STCSM) (13dz2260400), the National Science Foundation of China (11271049, 61501188, 61372147), and the Science Foundation of Shanghai (15ZR1410200).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the 973 Program [grant number 2011CB707104], the Science and Technology Commission of Shanghai Municipality (STCSM) [grant number 13dz2260400]; the National Science Foundation of China [grant number 11271049], [grant number 61501188], [grant number 61372147]; and the Science Foundation of Shanghai [grant number 15ZR1410200].