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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/9/1972 |
| _version_ | 1811301217198931968 |
|---|---|
| author | Xiaoqian Yang Zhenhong Jia Jie Yang Nikola Kasabov |
| author_facet | Xiaoqian Yang Zhenhong Jia Jie Yang Nikola Kasabov |
| author_sort | Xiaoqian Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T07:05:01Z |
| format | Article |
| id | doaj.art-f4e1e17489fc4abaabb8ffe6a74fcf4e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:05:01Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4e1e17489fc4abaabb8ffe6a74fcf4e2022-12-22T02:57:02ZengMDPI AGSensors1424-82202019-04-01199197210.3390/s19091972s19091972Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution AlgorithmXiaoqian Yang0Zhenhong Jia1Jie Yang2Nikola Kasabov3College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumuqi 830046, ChinaInstitute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, ChinaKnowledge Engineering and Discovery Research Institute, Auckland University of Technology, 1020 Auckland, New ZealandThe 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.https://www.mdpi.com/1424-8220/19/9/1972optical remote sensing imagethin cloud removalcombination difference mapFCM clusteringunsupervisedchange detection |
| spellingShingle | Xiaoqian Yang Zhenhong Jia Jie Yang Nikola Kasabov Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm Sensors optical remote sensing image thin cloud removal combination difference map FCM clustering unsupervised change detection |
| title | Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm |
| title_full | Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm |
| title_fullStr | Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm |
| title_full_unstemmed | Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm |
| title_short | Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm |
| title_sort | change detection of optical remote sensing image disturbed by thin cloud using wavelet coefficient substitution algorithm |
| topic | optical remote sensing image thin cloud removal combination difference map FCM clustering unsupervised change detection |
| url | https://www.mdpi.com/1424-8220/19/9/1972 |
| work_keys_str_mv | AT xiaoqianyang changedetectionofopticalremotesensingimagedisturbedbythincloudusingwaveletcoefficientsubstitutionalgorithm AT zhenhongjia changedetectionofopticalremotesensingimagedisturbedbythincloudusingwaveletcoefficientsubstitutionalgorithm AT jieyang changedetectionofopticalremotesensingimagedisturbedbythincloudusingwaveletcoefficientsubstitutionalgorithm AT nikolakasabov changedetectionofopticalremotesensingimagedisturbedbythincloudusingwaveletcoefficientsubstitutionalgorithm |