<p>This article presents a novel post-processing method for identifying buildings in high-resolution satellite imagery using Morphological Spatial Pattern Analysis (MSPA). Due to the lack of unique spectral signatures, buildings are often difficult to distinguish from other features such as vegetation and water bodies. The proposed method initially uses the Morphological Building Index (MBI) to detect buildings based on characteristics such as size, contrast and brightness. However, this approach can result in false alarms from other features such as roads, vegetation and open areas. To overcome this, several post-processing techniques are incorporated, including the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) and Hue filtering. Nonetheless, these techniques may still misclassify open areas and shadows as buildings. To further refine the results, MSPA is proposed, which segments the MBI output into seven distinct spatial categories, retaining only those corresponding to actual building structures. The proposed approach is evaluated on three subset images from the WHU-Satellite dataset, covering the cities of Milan, Wuhan, and Taiwan, as well as on two additional subset images from the Massachusetts Buildings Dataset for the Boston area. The proposed method yields overall classification accuracies of 86.14, 92.51, 89.62, 90.16, and 91.23%, respectively, across the five evaluated subset images.</p>