Multi-class classification of vegetation in natural environments using an Unmanned Aerial system
Alistair Reid, Fabio Tozeto Ramos, Salah Sukkarieh · 2011
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.