Detection of cars has a high variety of civil and military applications, e.g., transportation control, traffic monitoring or surveillance. It forms an important aspect in the deployment of autonomous Unmanned Aerial System (UAS) in rescue or surveillance missions. In this paper, we present a two-stage algorithm for detecting automobiles in aerial digital images. In the first stage, a feature based detection, based on local histogram of oriented gradients (HOG) and support vector machine (SVM) classification, is performed. Next, a generative statistical model is used to generate a ranking for each patch. The ranking can be used as a measure of confidence or threshold to eliminate those patches that are least likely to be an automobile. We analyze the results obtained from three different types of data sets. In various experiments we present the performance improvement of this approach compared to a discriminative- only approach, e.g., the false alarm rate is reduced by a factor of seven with only ten percent drop in the recall rate.