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http://dx.doi.org/10.7780/kjrs.2019.35.3.2

Detection of Damaged Pine Tree by the Pine Wilt Disease Using UAV Image  

Lee, Seulki (Department of Integrated Science, Kangwon University)
Park, Sung-jae (Department of Integrated Science, Kangwon University)
Baek, Gyeongmin (Department of Integrated Science, Kangwon University)
Kim, Hanbyeol (Department of Integrated Science, Kangwon University)
Lee, Chang-Wook (Department of Integrated Science, Kangwon University)
Publication Information
Korean Journal of Remote Sensing / v.35, no.3, 2019 , pp. 359-373 More about this Journal
Abstract
Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.
Keywords
Pine wilt disease; UAV image; ANN; SVM; Classification;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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