Multi-class predictive template for tree crown detection

This paper presents a novel approach for automatic segmentation and object detection of tree crowns in airborne images captured from a low-flying Unmanned Aerial Vehicle (UAV) in ecology monitoring applications. Cost effective monitoring in these applications necessitates the use of vision-band-only imaging on the UAV platform; the reduction in spectral resolution (compared to multi- or hyper-spectral imaging) is balanced by the high spatial resolution available ( ∼ 20 cm/pixel) from the low-flying UAV, when compared to existing satellite or manned-aerial survey data. Our approach to object detection thus uses both geometry and appearance information (through the use of tree shape and shadow information) in addition to spectral information to help accurately distinguish tree crowns within our application. A predictive geometric template for tree detection is constructed using on-board UAV navigation data, sun lighting information and information about the geometry of the target crown. A two-stage detection algorithm is then used to segment tree crowns based on spectral (colour) information convolved with information from the predictive template. Results of our approach are presented using airborne image data collected from a fixed-wing UAV during a weed monitoring and mapping mission over farmland in West Queensland, Australia.