This thesis proposes a computer vision framework to enable improved operations of Remotely Piloted Aircraft equipped with onboard image sensors. The main use of payload image sensors is to provide visual imagery data to the system for real-time or post-processing applications; the application of an image quality metric and the ground sampling distance of the image sensor can be used to predict the performance of an image sensor in enabling the image classification task. This information is used to determine the mission-specific operational envelope of the aircraft, to ensure that visual data quality requirements are met. The application of a convolutional neural network for image processing is also presented. Finally, a vision-based positioning system is developed, and it achieves an average position estimation difference of 17.87 cm compared to a commercially available indoor localization system; this system provides a position update rate of 11.48 Hz.