Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43818
Title: An object-based change detection approach using uncertainty analysis for VHR images
Authors: Hao, M
Shi, W 
Deng, K
Zhang, H
He, P
Issue Date: 2016
Publisher: Hindawi Publishing Corporation
Source: Journal of sensors, 2016, v. 2016, 9078364 How to cite?
Journal: Journal of sensors 
Abstract: 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.
URI: http://hdl.handle.net/10397/43818
ISSN: 1687-725X
EISSN: 1687-7268
DOI: 10.1155/2016/9078364
Rights: 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.
The following article: Hao, M., Shi, W., Deng, K., Zhang, H., & He, P. (2016). An object-based change detection approach using uncertainty analysis for VHR images. Journal of Sensors, 2016, is available at https//doi.org/10.1155/2016/9078364
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