While Very High-Resolution (VHR) imagery is favored for change detection due to its spatial detail, it presents challenges, notably intricate feature interactions and noise, complicating precise change identification. Addressing this, this paper introduces an unsupervised method for detecting building changes in Very High-Resolution (VHR) images, integrating the strengths of Principal Component Analysis (PCA) and K-Means clustering with a focus on building changes. Initially, PCA is employed to reduce data dimensionality, emphasizing the most significant variations across temporal datasets. The difference between the PCA-transformed images is computed, revealing areas of potential change. K-Means clustering then categorizes these regions based on their pixel values, labeling them as either changed or unchanged. A unique step in our approach is the building index extraction. This step refines the building detection by identifying contours in the segmented images based on their properties, such as area and perimeter emphasizing true building alterations and filtering out unrelated landscape changes. Experimental results on benchmark datasets, LEVIR-CD and CLCD, showcase the superior performance of the method, with an overall accuracy of 0. 97 and a Kappa coefficient of 0. 89. These results highlight the effectiveness of the proposed approach for building change detection in remote sensing and urban monitoring applications.