Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering. Issue 10 (19th May 2018)

• Record Type: Journal Article
• Authors: Yan, Weidong; Shi, Shaojun; Pan, Lulu; Zhang, Gang; Wang, Liya
• Is Part Of: International journal of remote sensing. Volume 39:Issue 10(2018)
• Page Start: 3055
• Page End: 3075
• Publication Date: 2018-05-19
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1434325
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c -means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.