Automatic classification of trees using a UAV onboard camera and deep learning

TL;DR

This study demonstrates that a cost-effective UAV-based deep learning system can accurately classify seven tree species using only RGB images, offering a practical tool for forest management.

Contribution

The paper presents a novel approach combining UAV imagery and deep learning for tree classification without expensive multispectral sensors.

Findings

Achieved 89.0% classification accuracy for 7 tree types

Used only basic RGB images from a standard UAV

Proved cost-effective method suitable for forest management

Abstract

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.