Yang Xiaoqian, Jia Zhenhong, Yang Jie, Kasabov Nikola
College of Information Science and Engineering, Xinjiang University, Urumuqi 830046, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2019 Apr 26;19(9):1972. doi: 10.3390/s19091972.
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.
本文首次研究了薄云干扰下光学遥感图像变化检测问题。首先，对光学遥感图像进行薄云去除处理，然后对处理后的遥感图像进行图像变化检测。基于对薄云图像特征的分析，本文提出了一种基于小波系数替换的薄云去除方法。基于小波系数的变化，分别对遥感图像的高频和低频部分进行替换，在抑制低频云的同时保持图像的高频细节，取得了良好效果。然后，应用基于组合差异图和模糊c均值聚类算法（FCM）聚类的无监督变化检测算法。首先，将图像转换到对数域，使用弗罗斯特滤波对图像进行去噪。然后，采用均值比法和差分法得到两个图差异图，采用组合差异图法得到最终差异图像。实验结果表明，该算法能有效解决薄云干扰下的图像变化检测问题。
