基于SfM的针叶林无人机影像树冠分割算法

利用无人机影像进行森林资源调查具有作业快速便捷、数据分辨率较高、影像细节丰富的特点,可较好地识别单木,获取树木位置、冠幅等信息。但是,厘米级的影像分辨率使基于光谱信息的传统分割算法在提取树冠时出现破碎化现象,产生过分割结果。同时,在非落叶季由于无人机影像难以观测到茂密林冠下层地形,故在地形起伏较大的林区难以实现基于树木冠层高度模型(CHM)的单木分割方法。针对上述问题,结合传统二维图像处理和SfM三维建模,提出了一种无需高度归一化的无人机影像树冠三维分割提取算法,首先利用SfM技术从高重叠航片建立三维表面模型,利用高程和图像信息检测初始树木位置,再采取kNN自适应邻域分水岭分割的方式对中心单木进行精确的树冠参数提取。在北京市百花山国家级自然保护区的落叶松林地进行了高分辨率无人机影像实验,采用正射影像目视解译结果和多种基于图像、点云的自动分割算法结果进行验证和评价。结果表明,本文方法对树木总体检出率在91%以上,冠幅提取精度在81%以上,优于传统的全局分水岭方法和其他树冠分割算法。

Coniferous Forest Crown Segmentation Algorithm of UAV Images Based on SfM

Using unmanned aerial vehicle (UAV) images to inventory forest resource is a quick solution to collect high resolution data with rich imagery details. It is capable to recognize individual trees with locations and crown sizes. An intrinsic problem of high spatial resolution UAV images at centimeter levels is that the images are tended to over segmented. In addition, UAV images captured in plant growing season can hardly observe the ground and objects beneath the canopy top, leading to infeasibility of height normalized canopy height model (CHM) based crown segmentation algorithms in forested areas with large terrain variations. To tackle these problems, a novel UAV image crown extraction approach was proposed, which was free of height normalization. Firstly, a 3D surface model was built from dense images by structure from motion technology. Initial tree locations were identified by combining height information and image contexts. An adaptive kNN neighborhood watershed algorithm was implemented to derive crown coverage of each initial tree locations. UAV images of Larch forests in Baihuashan National Nature Reserve of Beijing were used to conduct the experiment, and it was validated by visual interpretation on orthophotos and compared with a couple of images or point cloud based automatic segmentation algorithms. The results showed that the overall detection rate of individual trees was over 91%. The crown size extraction accuracy was over 81%, which outperformed the original watershed and other crown segmentation methods. It was demonstrated that the proposed method can serve to extract high accuracy tree parameters rapidly at large scales in complex terrain environment.