基于无人机遥感技术的台风灾害倒伏绿化树木检测

以福州大学为试验区，提出一种基于无人机遥感影像的台风灾害倒伏绿化树木的快速提取方法，为园林部门进行台风灾害损失评估、灾后重建提供参考。首先利用无人机遥感技术获取高于10 cm分辨率的台风过境前后影像，经过处理得到数字正射影像（Digital Orthophoto Map，DOM）和数字表面模型（Digital Surface Model，DSM）；其次采用高斯高通滤波算法突出树干的边缘信息；然后采用对比过滤分割算法结合互信息最大化特征选择算法（maximum Relevance Minimum Redundancy，mRMR）选择最佳特征子集，再分别根据阈值和随机森林（Random Forest，RF）分类方法检测出树干与非树干；最后使用骨架化算法将倒下的树干简化为骨架线，采用八邻域追踪法对单棵树干进行精细提取。结果表明：基于单期无人机影像使用阈值分类方法在试验区中共检测出了71棵倒伏木，准确率达76.06%；而基于RF分类方法倒伏木提取准确率虽提高了12.73%，但漏检率达25.39%；为了比较基于单期和两期影像两种数据源倒伏木的检测效率，结合两期DSM差值，分别采用阈值分类和RF分类两种方法，准确率分别为89.66%和87.30%，漏检率为17.46%和12.70%。研究认为，通过单时相影像特征基本能够检测出倒伏木，多时相影像分析可以有效提高倒伏木的检测精度，为不同数据源情况下的倒伏木检测提供了一种新途径。基于无人机遥感技术可以较好地实现台风灾后倒伏木数量的快速估算。

Detection of Lodging Landscape Trees in Typhoon Disaster based on Unmanned Aerial Vehicle Remote Sensing

Fuzhou University was taken as an experimental area， this paper presented a fast extraction method of lodging landscape trees in typhoon disaster based on unmanned aerial vehicle remote sensing image， which can provide reference for the assessment of typhoon disaster losses and post-disaster reconstruction of the landscape department. Firstly， unmanned aerial vehicle remote sensing technology was used to obtain Pre and Post images during typhoon passing with a resolution higher than 10cm. After processing， Digital Orthophoto Map（DOM）and Digital Surface Model（DSM）were obtained. Then gaussian high pass filtering algorithm was used to highlight the edge information of tree trunk. And the best feature subset was selected by contrast filtering segmentation algorithm combined with maximum Relevance Minimum Redundancy（mRMR）feature selection algorithm. In addition， the tree trunk and non-tree trunk were detected according to the threshold value and Random Forest（RF）classification method respectively. At last， the tree trunk of lodging tree was simplified into skeleton line by using skeletonization algorithm， and the single tree trunk was extracted by using octo-neighborhood tracking method. The results show that a total of 71 lodging trees were detected using threshold classification based on single-phase UVA images， with an accuracy of 76.06% in the experimental area. However， the accuracy of lodging tree extraction improved by 12.73% based on RF classification，and the missed detection reached 25.39%. In order to compare the detection efficiency of lodging trees based on single-phase and two-phase images， combined the difference value of DSM in the two phases， threshold and RF classification were used respectively， with an accuracy of 89.66% and 87.30%， a commission of 17.46% and 12.70%．Research suggests that the single-phase image features can basically detect the lodging trees， and the multi-phase images analysis can effectively improve the detection accuracy of the lodging trees， providing an effective reference for the detection of the lodging trees under different data sources. According to the research， the UAV remote sensing technology can realize the rapid estimation of the number of lodging trees after typhoon.