Visual Drone Terrain Classification: A Manual Classification Approach

This research investigates a method for performing manual classification of the terrain in imagery from the on-board camera of an unmanned aerial vehicle, to develop classifiers for systematic terrain classification. Drone images were captured across rural County Donegal in Ireland, and software was developed to manually label the terrain in these images, labelled in a lattice of 30 x30-pixel tiles. This dataset was used to train both a classic computer vision model and a Convolutional Neural Net model to classify the type of terrain under the UAV. The accuracy of the computer vision approach to the classification was compared to that of a Convolutional Neural Network trained using the Semantic Segmentation approach. The Convolutional Neural Network classifier was found to be the most accurate approach, achieving an fl score of 0.95.