Research on individual tree crown detection and segmentation using UAV imaging and Mask R-CNN

Abstract

Detecting and segmenting the single tree crown using the unmanned aerial vehicle ( UAV) remote sensing ima ges and obtaining parameters such as tree crown width (CW) and tree crown area (CA) can provide an efficient and fast way for forestry resources investigation in different urban scenes. At the same time, it can generate reference value for the estimation of urban tree health and growth status. The Ginkgo biloba tree in the city was selected as the research object, in which, the single G. biloba tree crown data set was obtained from UAV remote sensing images. The convolu- tional neural network Mask R-CNN algorithm and digital orthophoto map were used to detect the tree crown and draw the tree crown boundary to obtain the relevant tree crown parameters of different scenes in the city. The experimental re sults showed that the Mask R-CNN network model trained with the UAV G. biloba tree crown image data set can be well applied to the detection and segmentation of single G. biloba tree crown in different scenes in the city. In the four test scenes, the overall precision rate of the 86 single G. biloba tree crown targets reached 93.90%, and the recall rate reached 89.53%, F1-score was 91.66%, and mean average precision was 90.86%. In addition, the tree crown width and tree crown area of the single G. biloba tree crown can be accurately extracted. The average relative error (ARE) and root mean square error (RMSE) were 7.50% and 0.55 for the predicted tree crown width, and 11.15% and 2.48 for the predicted tree crown area, respectively. It was shown that the application of UAV images and appropriate deep learning algorithm in the urban forestry resource investigation can obtain accurate tree crown detection and contour segmentation, and effectively improve the efficiency of urban forestry resource investigation. The method of this study can also provide technical support for the extraction of relevant tree parameters in the city.