Multi-class classification of vegetation in natural environments using an Unmanned Aerial system

This paper presents an automated approach for the classification of vegetation in natural environments based on high resolution aerial imagery acquired by a low flying Unmanned Aerial Vehicle (UAV). Standard colour and texture descriptors are extracted on a frame by frame basis to build a representation of appearance, which is probabilistically classified by a novel multi-class generalisation of the Gaussian Process (GP) developed for this work. A GP approach was selected for probabilistic outputs, and the ability to automatically determine the relevance of each input dimension to each of the C classes in the problem. When learning hyperparameters from N training examples, the new formulation scales at O(N ), rather than O(CN3) for the standard one-vs-all approach. The novel classification framework is trained and validated on a set of manual labels, and then queried to visualise a map of vegetation type under the UAV flight path. Mapping results are presented for a region of farmland in Northern Queensland, Australia that is infested with two invasive introduced tree species.