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 Mar 7;19(5):1179. doi: 10.3390/s19051179.
The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.
传统 ROF 模型在图像去噪中的显式解存在结果不稳定和需要多次迭代的缺点。为了解决这个问题，本文提出了一种新的方法，即 ROF 模型半隐式去噪，并将其应用于合成孔径雷达（SAR）图像的变化检测中。本文中使用的所有遥感图像都已通过 ENVI 软件进行校准。首先，使用 ROF 模型半隐式去噪方法对遥感图像进行去噪。其次，对于去噪后的图像，通过对数比和均值比方法获得差值图像。最后，通过对两个差值图像的主成分分析融合（PCA 融合）得到最终的差值图像。最后，通过模糊局部信息 C 均值聚类（FLICM）对最终的差值图像进行聚类，得到变化区域。研究结果表明，该方法具有较高的检测精度和时间运算效率。
