关键词: 无人机图像分析, 森林病虫害, 枯死树监测, 纹理特征提取, 超像素, 线性谱聚类

Abstract: [Objective] In this paper, the method based on linear spectral clustering (LSC) superpixel was applied in the field of forest pest control, which was able to intelligently monitor dead trees in forest pest images taken from the unmanned aerial vehicle (UAV), and provide technological support for intelligently monitoring of forest pests.[Method]The UAV images of pine forests infected by Bursaphelenchus xylophilus and Dendroctonus valens respectively from Hubei and Liaoning provinces were chosen as the experiment data. Firstly, the linear spectral clustering superpixel algorithm was used to divide the image into many compact and uniform superpixels. Then, on a basis of the different color characteristics of dead trees and healthy trees, the superpixels which might be dead trees were initially extracted. Next, based on the different texture features of dead trees and other red disturbances, the area density and the lacunarity of the initially extracted superpixels were calculated. Finally, the support vector machine based on texture features was used to classify the initially extracted superpixels to detect dead trees in the image.[Result]The method based on LSC superpixel was able to exclude other interference objects that were similar in color to dead trees, and accurately extracted dead trees. The 35 UAV images of the pest-infected pine forest were used for comparing quantitatively this method with the other two methods. One is threshold segmentation method based on vegetation color index, and the other is simple linear iterative clustering (SLIC) superpixel and random forest method. Furthermore, the three evaluation indexes:intersection over union, the false alarm rate and the misdetection rate were used to quantitatively compare and analyze the three methods. The experimental results showed that the algorithm based on LSC superpixel and SVM was the most accurate to detect dead trees. The mean of intersection over union between this method's result and manual detection result was more than 58% and the false alarm rate and the misdetection rate were better than the other two algorithms.[Conclusion] Our results showed that the dead tree monitoring method based on LSC superpixel was able to detect and locate dead trees quickly and precisely in the UAV pine forest images and effectively protect forest resources.

Key words: analysis of unmanned aerial vehicle image, forest pest, dead tree monitoring, texture feature extraction, superpixel, linear spectral clustering