A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection. Issue 8 (17th April 2016)

• Record Type: Journal Article
• Authors: Jian, Ping; Chen, Keming; Zhang, Chenwei
• Is Part Of: International journal of remote sensing. Volume 37:Issue 8(2016)
• Page Start: 1814
• Page End: 1825
• Publication Date: 2016-04-17
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/2150704X.2016.1163744
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: This article presents a novel semi-supervised change detection approach for very-high-resolution (VHR) remote-sensing images. The proposed approach aims at extracting the change information by making full use of the context-sensitive relationships among pixels in the images. This is accomplished via a context-sensitive image representation technique based on hypergraph model. First, each temporal image is modelled as a hypergraph that utilizes a set of hyperedges to capture the context-sensitive properties of pixels in the image. Second, the difference in the bi-temporal images is measured by both the similarity and the consistency between the two hypergraphs. Finally, the changes are separated from the unchanged ones by a hypergraph-based semi-supervised classifier on the difference image. Experimental results obtained on different VHR remote-sensing data sets demonstrate the effectiveness of the proposed approach.