Unmanned aerial vehicles, known as UAVs, have evolved over the past two decades to sophisticated aircraft robots able to carry out surveillance, recognition, remote sensing and even attack missions. But there are not many alternatives of autonomous navigation systems for most of these aircraft which still require human intervention to navigate. Devices such as Global Positioning System (GPS) and inertial systems help calculate routes and locate the vehicle on a map among other possibilities, but do not offer solutions to unknown or uncertain circumstances. On the other hand, computer vision techniques have provided many possible applications for intelligent systems such as object recognition, robot localization and reconstruction of 3D maps. This paper explores the use of computer vision and pattern recognition techniques for UAV navigation, and proposes a set of visual features based on color and gradients orientation for image classification. To validate the proposed approach, a system was developed to evaluate the classification and matching of aerial images. The results achieve more than 95% of accuracy and confirm the viability of the selected algorithms and methods for the problem.