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Journal of Sensors
Volume 2016, Article ID 9078364, 17 pages
Research Article

An Object-Based Change Detection Approach Using Uncertainty Analysis for VHR Images

1School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan 430079, China
3A Joint Research Laboratory on Spatial Information, Wuhan University and The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong

Received 7 May 2015; Revised 19 November 2015; Accepted 30 November 2015

Academic Editor: Gyuhae Park

Copyright © 2016 Ming Hao et al. 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.


This paper proposes an object-based approach to supervised change detection using uncertainty analysis for very high resolution (VHR) images. First, two temporal images are combined into one image by band stacking. Then, on the one hand, the stacked image is segmented by the statistical region merging (SRM) to generate segmentation maps; on the other hand, the stacked image is classified by the support vector machine (SVM) to produce a pixel-wise change detection map. Finally, the uncertainty analysis for segmented objects is implemented to integrate the segmentation map and pixel-wise change map at the appropriate scale and generate the final change map. Experiments were carried out with SPOT 5 and QuickBird data sets to evaluate the effectiveness of proposed approach. The results indicate that the proposed approach often generates more accurate change detection maps compared with some methods and reduces the effects of classification and segment scale on the change detection accuracy. The proposed method supplies an effective approach for the supervised change detection for VHR images.