Unsupervised change detection in SAR images using curvelet and L1-norm based soft segmentation. Issue 14 (17th July 2016)

• Record Type: Journal Article
• Authors: Li, Fang; Fang, Faming; Zhang, Guixu
• Is Part Of: International journal of remote sensing. Volume 37:Issue 14(2016)
• Page Start: 3232
• Page End: 3254
• Publication Date: 2016-07-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1196838
• Languages: English
• ISSNs: 0143-1161

Abstract

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.