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, researchers at Kyoto University constructed a machine vision system for the automatic classification of trees.
They 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 they 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 their 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.
Source: Kyoto University Research Paper