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Remote Sens. 2016, 8(9), 745; doi:10.3390/rs8090745

A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

1
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
3
Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YQ, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Xiaofeng Li and Prasad S. Thenkabail
Received: 22 June 2016 / Revised: 27 August 2016 / Accepted: 5 September 2016 / Published: 12 September 2016
(This article belongs to the Special Issue Uncertainties in Remote Sensing)
View Full-Text   |   Download PDF [5930 KB, uploaded 12 September 2016]   |  

Abstract

Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD. View Full-Text
Keywords: change detection; statistical region merging; Dempster-Shafer evidence theory; uncertainty analysis change detection; statistical region merging; Dempster-Shafer evidence theory; uncertainty analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Hao, M.; Shi, W.; Zhang, H.; Wang, Q.; Deng, K. A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images. Remote Sens. 2016, 8, 745.

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