In this paper, an unsupervised change detection (CD) approach based on arc-tangential difference and k-Means++ clustering is presented for synthetic aperture radar (SAR) remote sensing images. The images are first standardized with their variance values using a logarithmic function applied to multi-temporal images. The difference image is then calculated by subtracting the SAR images using the arc-tangential subtraction operator. After that, the difference image is subjected to a 2D Gaussian filter and a median filter, respectively. Filters are essential for determining the best feature space for CD. The 2D Gaussian filter smooths difference images to retain local area consistency, while the median filter handles edge information. Finally, using k-Means++, a quick and efficient clustering approach, filtered data is clustered into two classes. Experiments using real-world datasets in Bern, Ottawa, and Yellow River have demonstrated that the given technique is fast, successful, and effective.