This paper presents a semantic method for aerial image segmentation. Multi-class aerial images are often featured with large intra-class variations and inter-class similarities. Furthermore, shadows, reections and changes in viewpoint, high and varying altitude and variability of natural scene pose serious problems for simultaneous segmentation. The main purpose of segmentation of aerial images is to make subsequent recognition phase straightforward. Present algorithm combines two challenging tasks of segmentation and classification in a manner that no extra recognition phase is needed. This algorithm is supposed to be part of a system which will be developed to automatically locate the appropriate site for Unmanned Aerial Vehicle (UAV) landing. With this perspective, we focused on segregating natural and man-made areas in aerial images. We compared different classifiers and explored the best set of FEATURES for this task in an experimental manner. In addition, a certainty based method has been used for integrating COLOR and texture descriptors in a more efficient way. The experimental results over a dataset comprised of 25 high-resolution images show the overall binary segmentation accuracy rate of 91.34%.