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

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...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 19; číslo 9; s. 1972
Hlavní autoři: Yang, Xiaoqian, Jia, Zhenhong, Yang, Jie, Kasabov, Nikola
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 26.04.2019
MDPI
Témata:
Algorithms
Atmospheric aerosols
change detection
Clouds
Clustering
combination difference map
FCM clustering
Fog
International conferences
Noise
optical remote sensing image
Remote sensing
Satellites
thin cloud removal
unsupervised
Wavelet transforms
China
combination difference map
change detection
thin cloud removal
optical remote sensing image
unsupervised
FCM clustering
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19091972