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Open AccessArticle

Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm

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College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China
2
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
3
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, 1020 Auckland, New Zealand
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 1972; https://doi.org/10.3390/s19091972
Received: 25 March 2019 / Revised: 20 April 2019 / Accepted: 25 April 2019 / Published: 26 April 2019
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference. View Full-Text
Keywords: optical remote sensing image; thin cloud removal; combination difference map; FCM clustering; unsupervised; change detection optical remote sensing image; thin cloud removal; combination difference map; FCM clustering; unsupervised; change detection
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Yang, X.; Jia, Z.; Yang, J.; Kasabov, N. Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm. Sensors 2019, 19, 1972.

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