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Urban cinnamomum camphora crown detection research using RGB-DSM images and deep learning

  • role： First author第一作者
  • Affiliation：

    College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China

    Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China

    Key Laboratory of Forestry Perception Technology and Intelligent Equipment, State Forestry Administration, Hangzhou 311300, China

  • Email：524285019@qq.com
  • Introduction：王昊，研究方向为林业信息化。E-mail： 524285019@qq.com
WANG Hao,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China

    Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China

    Key Laboratory of Forestry Perception Technology and Intelligent Equipment, State Forestry Administration, Hangzhou 311300, China

  • Email：xiakai@zafu.edu.cn
  • Introduction：夏凯，研究方向为机器学习，遥感及林业信息化。E-mail： xiakai@zafu.edu.cn
XIA Kai*,  
  • Affiliation：

    College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China

    Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China

    Key Laboratory of Forestry Perception Technology and Intelligent Equipment, State Forestry Administration, Hangzhou 311300, China

YANG Yinhui,  
  • Affiliation：

    College of Mathematics and Computer Science, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China

    Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, China

    Key Laboratory of Forestry Perception Technology and Intelligent Equipment, State Forestry Administration, Hangzhou 311300, China

FENG Hailin

résumé

Currently, combining remote sensing imagery with deep learning is a growing trend in individual tree crown detection. RGB image is the most commonly used data type in detection. However, given that the color and texture of the tree crowns are generally close, distinguishing the crowns of different individuals by using only the color and texture information of RGB image in areas with high density of crowns is difficult. In this study, the elevation information is superimposed to improve the accuracy of individual tree crown detection by using RGB images. In the experiment, RGB image (color image) and DSM (digital surface model) were used as data sources, and band combination and double-source detection network model were used to combine RGB and DSM for individual tree crown detection. In the former method, band combination of RGB and DSM was conducted to generate GBD, RGD, and RBD images, and the three kinds of images were used for network training and testing. In the latter method, RGB and DSM were input into the double-source detection network model, and the detection results were obtained. FPN-Faster-R-CNN and Yolov3 were used for experiments in this study. Compared with RGB scheme as the control scheme (which uses only the color and texture information of ground objects for individual tree crown detection), the average accuracy of FPN-Faster-R-CNN in the GBD scheme, RBD scheme, and double-source detection network scheme increased by 3.36%, 2.45%, and 7.77%, respectively; it decreased by 0.17% in the RGD scheme. The average accuracy of Yolov3 in the GBD scheme, RBD scheme, and double-source detection network scheme increased by 0.72%, 0.14%, and 5.71%, respectively; it decreased by 0.98% in the RGD scheme. Under the two networks, the double-source detection network scheme achieved the best detection result in each scheme. Compared with the RGB scheme, the improvement in average accuracy of double-source detection network scheme showed a rising trend with the increase in forest density. Comparative analysis of the experimental results shows that proper combination and utilization of the color, texture, and elevation information of the ground objects is beneficial to improve the performance in the urban individual tree crown detection task based on deep learning.

mots-clés

remote sensing;individual tree crown detection;deep learning;urban;elevation;color image;UAV

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