Abstract
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.
Highlights
Image change detection refers to the detection of landform changes in remote sensing images obtained at different times in the same area
The process of simulating adding clouds is as follows: a real thin cloud is added by software to any region of the optical remote sensing image to simulate an optical remote sensing image disturbed by thin cloud
In order to solve the problem of change detection of optical remote sensing image disturbed by thin cloud, this paper proposed, for the first time, an unsupervised change detection method based on combination difference map and fuzzy c-means clustering algorithm (FCM) clustering combined with wavelet coefficient substitution algorithm
Summary
Image change detection refers to the detection of landform changes in remote sensing images obtained at different times in the same area. In order to solve the problem that optical remote sensing images are affected by thin clouds, a new cloud removal algorithm combined with an image change detection method is proposed in this paper. The experimental results of the three groups (two real cloud images and one artificial simulation with added thin clouds) show that the method can effectively suppress the interference of optical remote image cloud noise and enhance the useful details. Since the image has different degrees of noise interference after removing the thin cloud, in order to improve the detection accuracy of optical remote sensing images, an unsupervised change detection algorithm based on a combination of the difference image and FCM (fuzzy c-means clustering algorithm) clustering is proposed in this paper. The experimental results show that the algorithm greatly reduces the difficulty of optical remote sensing image change detection under cloud interference and improves the detection accuracy
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