您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Deep learning-based dead pine tree detection from unmanned aerial vehicle images

文献类型： 外文期刊

作者： Tao, Huan 1 ; Li, Cunjun 1 ; Zhao, Dan 3 ; Deng, Shiqing 1 ; Hu, Haitang 1 ; Xu, Xinluo 1 ; Jing, Weibin 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing Key Lab Environm Damage Assessment & Reme, Beijing, Peoples R China

3.Chinese Acad Environm Planning, Ctr Environm Risk & Damage Assessment, Beijing, Peoples R China

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.151； 五年影响因子：3.266 ）

ISSN： 0143-1161

年卷期： 2020 年 41 卷 21 期

页码：

收录情况： SCI

摘要： Understanding the spatial distribution of dead pine trees (DPTs) in mountainous areas is vital for diseased wood cleanup and the prediction ofpine wilt disease. The study induced a deep learning (DL) model of convolutional neural networks (CNNs) for the DPT detection using unmanned aerial vehicle images. Two thousand manually labelled DPT and Non-DPT samples were collected to train (80%) and optimize (20%) the CNNs of AlexNet and GoogLeNet, and 768 samples from other three areas were used to predict the categories in Jinjiang, Fujian, southeastern China. The same dataset was used to compare the classification accuracy between CNNs and the traditional template matching (TM) method. In addition, the potential factors influencing the overall accuracy of the DL model for DPT detection were evaluated. Training results of AlexNet and GoogLeNet showed an accuracy of over 93.30% and 97.38% respectively for the top-1 test after the final training iteration. For the DPT classification in the prediction areas of pure forests, the TM method showed the 56-65% of overall accuracy while CNNs showed 65-80%. CNNs-based DPT detection was more accurate than the traditional TM method. Terrains and red broadleaf trees influence the CNNs-based DPT detection from aerial visible-images in mixed artificial forest and peaks areas.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台