To reduce data acquisition cost, this study proposed a novel method of individual tree height estimation and canopy extraction based on fusion of an airborne multispectral image and photogrammetric point cloud. A fixed-wing drone was deployed to acquire the true color and multispectral images of a shelter forest. The Structure-from-Motion (SfM) algorithm was used to reconstruct the 3D point cloud of the canopy. The 3D point cloud was filtered to acquire the ground point cloud and then interpolated to a Digital Elevation Model (DEM) using the Radial Basis Function Neural Network (RBFNN). The DEM was subtracted from the Digital Surface Model (DSM) generated from the original point cloud to get the canopy height model (CHM). The CHM was processed for the crown extraction using local maximum filters and watershed segmentation. Then, object-oriented methods were employed in the combination of 12 bands and CHM for image segmentation. To extract the tree crown, the Support Vector Machine (SVM) algorithm was used. The result of the object-oriented method was vectorized and superimposed on the CHM to estimate the tree height. Experimental results demonstrated that it is efficient to employ point cloud and the proposed approach has great potential in the tree height estimation. The proposed object-oriented method based on fusion of a multispectral image and CHM effectively reduced the oversegmentation and undersegmentation, with an increase in the -score by 0.12–0.17. Our findings provided a reference for the health and change monitoring of shelter forests as well.

中文翻译：

---

基于机载多光谱影像与摄影测量点云融合的防护林树木冠层提取与高度估计

为了降低数据采集成本，本研究提出了一种基于机载多光谱图像和摄影测量点云融合的单株高度估计和冠层提取的新方法。部署了固定翼无人机以获取防护林的真实彩色和多光谱图像。运动结构 (SfM) 算法用于重建冠层的 3D 点云。过滤 3D 点云以获取地面点云，然后使用径向基函数神经网络 (RBFNN) 插值到数字高程模型 (DEM)。从原始点云生成的数字表面模型 (DSM) 中减去 DEM 以获得冠层高度模型 (CHM)。使用局部最大滤波器和分水岭分割处理 CHM 以进行冠提取。然后，采用面向对象的方法结合12波段和CHM进行图像分割。为了提取树冠，使用了支持向量机（SVM）算法。将面向对象方法的结果矢量化并叠加在 CHM 上以估计树高。实验结果表明，采用点云是有效的，并且所提出的方法在树高估计中具有很大的潜力。所提出的基于多光谱图像和 CHM 融合的面向对象方法有效地减少了过分割和欠分割，增加了 将面向对象方法的结果矢量化并叠加在 CHM 上以估计树高。实验结果表明，采用点云是有效的，并且所提出的方法在树高估计中具有很大的潜力。所提出的基于多光谱图像和 CHM 融合的面向对象方法有效地减少了过分割和欠分割，增加了 将面向对象方法的结果矢量化并叠加在 CHM 上以估计树高。实验结果表明，采用点云是有效的，并且所提出的方法在树高估计中具有很大的潜力。所提出的基于多光谱图像和 CHM 融合的面向对象方法有效地减少了过分割和欠分割，增加了-得分为 0.12–0.17。我们的研究结果也为防护林的健康和变化监测提供了参考。