# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 2 Related Work  
Proposed by [PERSON] and [PERSON] [29], the original mean shift algorithm was applied to clustering and data noise filtering. It was further proven to be effective by [PERSON] [30] for clustering and global optimization. Then it was widely exploited as a robust approach of image segmentation in feature space [31]. Moreover, it was intensively used for non-rigid object tracking in real time [32]. Due to its clear advances in image segmentation, mean shift was soon applied to remote sensing imageries [33; 34; 35]. For example, [PERSON] and [PERSON] [33] used means shift with an adaptive bandwidth to extract object-based features for high dimensional hyperspectral image urban classification by support vector machine (SVM). [PERSON] et al. [36] applied mean shift to airborne hyperspectral image twice: Firstly to differentiate short and tall stands, and secondly to segment individual tree crowns, to classify a temperate forest. [PERSON] [37] pioneered the adoption of mean shift for ALS point cloud segmentation, by which power lines and vegetation were differentiated in an urban area. [PERSON] et al. [38] combined mean shift with normalized cuts, to segment and classify 3D airborne lidar data in urban areas. [PERSON] et al. [PERSON] et al. (2018) extracted shorelines from integrated airborne lidar point clouds and aerial orthophotos using mean-shift segmentation. Table 1 lists the usage of mean shift for tree segmentation from airborne lidar data, and the settings used in the studies. [PERSON] et al. [PERSON] et al. (2018) firstly used mean shift to stratify forest vertical structure in 3D. The Epanechnikov kernel, implemented as a Cylinder shape, was chosen, and three discrete kernel bandwidths were empirically selected to stratify the forest into three layers. The algorithm was then used to extract individual trees [PERSON] et al. (2018). As the kernel shape and the ratio between horizontal and vertical components were fixed, there was only one parameter, kernel bandwidth, to be tuned. [PERSON] et al. [PERSON] et al. (2018) then adapted the method to detect individual tree crowns at different layers within tropical forests. Based on the previous method, an adaptive mean shift 3D segmentation (AMS3D) using an allometric function that defines the relationship between tree height and crown width and depth was proposed. The bandwidth model was adaptive to the allometric function; for example it would increase as the kernel moves upwards on higher trees. [PERSON] et al. [PERSON] et al. (2018) also used the cylindrical kernel with a horizontal Gaussian profile to extract local dense modes of points using a fixed bandwidth. Those local modes were intentionally oversegmented, and features were derived from those segmented clusters, then grouped via normalized cuts by measuring the similarity of clusters in terms of their spatial distribution and features. This methodology was further investigated to estimate the regeneration coverage under 5 m in a temperate forest ([PERSON] et al., 2018). The radius and height of the cylinder-shaped kernel were both set independently, and the sensitivity of radius and height were further tested. Apart from forest trees, the mean shift algorithm was also applied to urban trees by [PERSON] et al. [PERSON] et al. (2018). To fit the general tree shape, a tree crown model, i.e., the Pollock model, which can vary from a cone to an ellipsoid, was proposed as the mean shift kernel. In addition, the continuous adaptive mean shift (CamShift) concept was adopted with the assumption that higher trees would have wider crowns, and would benefit from a larger bandwidth. Therefore the bandwidth was set to be continuously adaptive to the tree height with a constant ratio, which was insensitive to tree size, shape and species, as found in the experiments. The advantage of adaptive mean shift for individual tree identification was further proved by [PERSON] et al. [PERSON] et al. (2018).