Integrating change magnitude maps of spectrally enhanced multi-features for land cover change detection. Issue 11 (3rd June 2021)

• Record Type: Journal Article
• Authors: Xing, Huaqiao; Zhu, Linye; Hou, Dongyang; Zhang, Tao
• Is Part Of: International journal of remote sensing. Volume 42:Issue 11(2021)
• Page Start: 4284
• Page End: 4308
• Publication Date: 2021-06-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1892860
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Constructing a change magnitude map (CMM) is a key component of binary change detection. Recently, integrating multiple features to obtain a comprehensive CMM has become a popular research topic. However, the current integration approaches mainly utilize simple spectral CMMs that are derived based on a single spectral change index (e.g. image difference, Euclidean distance, and change vector analysis), which is not sufficient for addressing complex land cover changes. In this study, we propose a spectrally enhanced multi-feature fusion (SeMF) method with CMM integration for effective change detection. Seven commonly used spectral change indices are analysed from the aspects of the spectral value and spectral shape; two of these indices are selected to construct the optimal spectral-based CMM, which is more efficient, robust and stable than the single spectral change indices. The rotation-invariant local binary patterns (RiLBP) and Canny methods are further used for CMM generation via the textural and shape features, respectively. These three types of CMMs are adaptively assigned weights by using an information entropy-based fusion strategy and ultimately integrated into a comprehensive CMM. Two groups of experiments with Landsat 8 Operational Land Imager (OLI) and Gaofen (GF)-1 images are designed to verify the effectiveness of the SeMF method. The experimental results indicate that the SeMF method is superior to both spectral feature-based and multi-feature-based change detection methods.