ICRA 2011 Paper Abstract


Paper WeA114.5

Reid, Alistair (University of Sydney), Ramos, Fabio (University of Sydney), Sukkarieh, Salah (University of Sydney)

Multi-Class Classification of Vegetation in Natural Environments Using an Unmanned Aerial System

Scheduled for presentation during the Regular Sessions "Computer Vision for Robotics and Automation I" (WeA114), Wednesday, May 11, 2011, 09:20−09:35, Room 5J

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Robotics in Agriculture and Forestry, Recognition


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) classifier. 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(N3), 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.



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