A hierarchical spatial-temporal graph-kernel for high-resolution SAR image change detection. Issue 10 (18th May 2020)

• Record Type: Journal Article
• Authors: Jia, Lu; Wang, Jincheng; Ai, Jiaqiu; Jiang, Ye
• Is Part Of: International journal of remote sensing. Volume 41:Issue 10(2020)
• Page Start: 3866
• Page End: 3885
• Publication Date: 2020-05-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1708507
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Effective utilization of structural information is important for high-resolution synthetic aperture radar (SAR) image change detection. For comprehensively utilizing the local and global structures in SAR images, a hierarchical spatial-temporal graph kernel (STGK) method is proposed in this paper for high-resolution SAR image change detection. First, the bi-temporal hierarchical graph models are constructed for extracting the local-global structures in the bi-temporal SAR images. Then, a STGK function, which measures the spatial and temporal similarities between the local-global structures, is constructed for indicating the change levels between the bi-temporal images. Finally, a support vector machine (SVM) is implemented with the STGK function for producing the final change detection results. Experimental results on real GaoFen-3 SAR data sets demonstrate the effectiveness of the proposed method, and prove that the STGK method is capable of detecting changed areas with relatively complex structures.