基于改进的Faster R-CNN模型的树冠提取研究

Research on Crown Extraction Based on Improved Faster R-CNN Model

树冠信息是森林资源调查中的重要内容。传统的树冠冠幅测量方法为实地调查,该方法测量结果在特定的地形和森林环境中误差较大,且人力消耗大、操作繁琐、耗时长。无人机影像技术和深度学习的发展为树冠测量提供了新的方法和实现思路。利用无人机获取了临安东部青山湖绿道两块纯水杉林样地的正射影像图,通过改进目前先进的目标检测方法Faster R-CNN进行树冠的识别和冠幅的提取。基于改进的Faster R-CNN模型准确率和决定系数达到了92.92%和0.84,分别比改进前的模型提高了5.31%和0.12。这说明了无人机和目标检测技术识别树冠的可行性,这一方法和传统的调查方法相比,具有高效、便捷和低成本的优势。

Canopy information is an important part of forest resources investigation.The traditional method of crown width measurement is through field survey,which may result in a significant error in the specific terrain and forest environment,along with great labor force,cumbersome and time-consuming operation procedure.The development of UAV imaging technology and machine learning provides a new method and realization idea for crown measurement.This paper employed UAV to obtain the orthophoto images of two pure Metasequoia glyptostroboides in the greenway of Qingshanhu in the east of Lin′an District.An advanced object detection method,Faster R-CNN,was improved to recognize the tree crown and extract the crown width.The Accuracy and R2 of the improved Faster R-CNN model are 92.92%and 0.84 respectively,which are 5.31%and 0.12 higher than those of the original model.This shows that the UAV and object detection technology are feasible to identify the tree crown.Compared with the traditional survey method,it has the advantages of high efficiency,convenience and low cost.