  Automatic classification of trees using remotely sensed data has been a dream
of many scientists and land use managers. Recently, Unmanned aerial vehicles
(UAV) has been expected to be an easy-to-use, cost-effective tool for remote
sensing of forests, and deep learning has attracted attention for its ability
concerning machine vision. In this study, using a commercially available UAV
and a publicly available package for deep learning, we constructed a machine
vision system for the automatic classification of trees. In our method, we
segmented a UAV photography image of forest into individual tree crowns and
carried out object-based deep learning. As a result, the system was able to
classify 7 tree types at 89.0% accuracy. This performance is notable because we
only used basic RGB images from a standard UAV. In contrast, most of previous
studies used expensive hardware such as multispectral imagers to improve the
performance. This result means that our method has the potential to classify
individual trees in a cost-effective manner. This can be a usable tool for many
forest researchers and managements.
