光学精密工程2023，Vol.31Issue(22)：3331-3344,14.DOI:10.37188/OPE.20233122.3331
融合机载LiDAR和植被指数的自适应单木提取方法
Adaptive single tree extraction method based on fusion of airborne lidar and vegetation index
摘要
Abstract
Airborne laser data(Light Detection and Ranging,LiDAR)presents challenges in distinguish-ing between ground and grassland,and visible light vegetation indices are inadequate for differentiating be-tween shrub and tree layers.Therefore,this study proposes the construction of a multi-band information image that integrates LiDAR point cloud data and RGB vegetation indices.The approach integrates multi-band information from LiDAR point cloud data and vegetation indices to create an enhanced image.The fine-grained canopy height model(CHM)is generated using laser point cloud data.Simultaneously,a high-resolution digital orthophoto image is created using unmanned aerial vehicle imagery data.Among the evaluated vegetation indices,the Differential Enhanced Vegetation Index(DEVI)was the most suitable and was fused with the CHM.Subsequently,the CHM+DEVI fused images were reconstructed to elimi-nate erroneous values.Training samples were constructed,and the classification regression tree algorithm was employed to segment the ground range and adaptively extract vegetation,such as trees,shrubs,and grasslands.Within the tree areas,the local maximum algorithm was applied to detect tree vertices,which served as foreground markers;meanwhile,the non-tree regions were assigned as background markers.The segmentation results were obtained using watershed transformation,and the accuracy of the extracted vegetation information was analyzed by comparing it with ground-truth data.The evaluation results dem-onstrate the superior performance of the proposed improved algorithm,with the overall recall rate,preci-sion rate,and accuracy F1 score increasing by 3.2%,3.9%,and 3.5%,respectively.Moreover,the ac-curacy of tree height measurements exhibited improvements of 1.7%,6.4%,1.8%,and 0.3%in the four quadrats.The effectiveness of the improved method was verified,and the higher the degree of vegeta-tion mixing in the region,the better the extraction effect of the improved algorithm.关键词
激光雷达/无人机影像/差异增强植被指数/形态学重建/标记分水岭算法Key words
light detection and ranging/UAV imagery/differential enhanced vegetation index/morpho-logical reconstruction/mark-controlled watered segmentation分类
计算机与自动化引用本文复制引用
代震,何荣,王宏涛,白伟森..融合机载LiDAR和植被指数的自适应单木提取方法[J].光学精密工程,2023,31(22):3331-3344,14.基金项目
NSFC-区域创新发展联合基金重点项目(No.U22A20566) （No.U22A20566）
河南省高等学校重点科研资助项目(No.18B420003) （No.18B420003）
河南理工大学基本科研业务费专项资助项目(No.NSFRF170909) （No.NSFRF170909）
