High-Resolution Remote-Sensing Image-Change Detection Based on Morphological Attribute Profiles and Decision Fusion. (23rd March 2020)

• Record Type: Journal Article
• Authors: Wang, Chao; Liu, Hui; Shen, Yi; Zhao, Kaiguang; Xing, Hongyan; Wu, Haotian
• Other Names: Eisencraft Marcio Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-03-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/8360361
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote sensing (HRRS) images based on morphological attribute profiles and decision fusion is proposed in this study. By establishing the objective function based on the minimum of average interscale correlation, a morphological attribute profile with adaptive scale parameters (ASP-MAPs) is presented to exploit the spatial structure information. On this basis, a multifeature decision fusion framework based on the Dempster–Shafer (D-S) theory is constructed for obtaining the CD map. Experiments of multitemporal HRRS images from different sensors have shown that the proposed method outperforms the other advanced comparison CD methods, and the overall accuracy (OA) can reach more than 83.9%.