A framework of spatiotemporal fuzzy clustering for land-cover change detection using SAR time series. Issue 2 (17th January 2017)

• Record Type: Journal Article
• Authors: Wang, Yafei; Zhao, Feifei; Chen, Peipei
• Is Part Of: International journal of remote sensing. Volume 38:Issue 2(2017)
• Page Start: 450
• Page End: 466
• Publication Date: 2017-01-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1268736
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Compared with optical satellite images, synthetic aperture radar (SAR) images are less influenced by weather conditions such as cloud and haze. With the support of SAR image time series, a framework of change detection based on spatiotemporal fuzzy clustering is presented. This framework mainly consists of three components: (1) pixel-level SAR image time-series modelling, based on scale invariant feature transform (SIFT); (2) probability analysis of change node based on iterative binary partition-mean square error model of the series is calculated to ascertain change nodes; (3) spatiotemporal fuzzy clustering is used to determine the types of change detection. To validate the method, 26 SAR images of the study area between 2004 and 2010 are utilized to monitor annual changes of cultivated land to construction land, and comparative experiments are conducted to evaluate the detection accuracy. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 84.52% and completeness of 82.64%, outperforming the traditional fuzzy clustering method, as well as traditional classification methods.