A land-cover change detection method using data-oriented composite-kernel-based one-class support vector machine. Issue 10 (3rd October 2017)

• Record Type: Journal Article
• Authors: Zhao, Lijun; Tang, Jiakui; Yu, Xinju; Tang, Ping
• Is Part Of: Geocarto international. Volume 32:Issue 10(2017)
• Page Start: 1049
• Page End: 1068
• Publication Date: 2017-10-03
• Subjects: Remote sensing -- information entropy -- change detection -- one-class support vector machine -- composite kernel; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2016.1195882
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: In many change detection applications, the focus is often on one specific change class. The one-class support vector machine (OCSVM)-based change detection method has been proved effective for dealing with such problems, which only requires samples from the change class of interest as the training data. However, this classical method only uses a single kernel which limits its separating capabilities in real-world applications. To further improve the efficacy of the OCSVM-based change detection method, this paper proposes an improved change detection method that uses a data-oriented composite-kernel-based one-class support vector machine. It utilizes the feature information entropy of the training data to determine the kernel weights in constructing a composite kernel. Experimental results on two data-sets demonstrate that the proposed method outperforms the existing classical OCSVM-based change detection method and the traditional composite-kernel-based method with relatively few false alarm errors, and shows good potential for further applications.