Towards intricate Stand Structure: A Novel Individual Tree Segmentation Method for ALS Point Cloud Based on Extreme Offset Deep Learning  
Yizhuo Zhang  
School of Computer Science, Changzhou University, Changzhou 213000, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (H.L. ); s211508120408 mail.cczu.edu.cn (X.L.) 1  
[PERSON]  
Correspondence: [EMAIL_ADDRESS] (H.L. ); s211508120408 mail.cczu. have great room for improvement [5; 6]. Therefore, it is particularly important to design an ITS method with high universality and high accuracy. From the perspective of segmentation strategy, ITS methods can be divided into two categories: bottom-up and top-down [7], and the top-down segmentation strategy is adopted in this paper. As the trunk points of ALS forest point cloud with high canopy density are sparse [8; 9], the bottom-up method is seriously affected by noise when selecting seed points, resulting in poor segmentation results. In the bottom-up method, the tree trunks in vertical space are extracted, and then the tree trunks are used as seed points to delineate the individual trees. Existing methods include the tree trunk detection fusion tree crown segmentation method [7], the two-dimensional tree trunk detection method [10], and the adaptive trunk detection method [11]. In general, due to the poor penetration rate of airborne radar pulses, the tree trunk points scanned in a high-canopy-density environment are sparse [8], and there may be a large deviation in the seed points, thus affecting the depiction of the canopy [7], leading to the poor segmentation of the bottom-up method in the high canopy density ALS forest point cloud. In the top-down method, canopy features (e.g., gradient characteristics among canopies, treetops, etc.) are identified to delineate the individual trees. Common methods include the spatial horizontal distance rule algorithm (SHDR) [12], which has high segmentation accuracy but high time complexity, and is very sensitive to the selection of the horizontal distance threshold parameter; therefore, it has poor universality. The mark-controlled watershed segmentation algorithm [13] uses the mark-controlled watershed method to segment individual trees; the segmentation accuracy is poor when dealing with mixed forests with unobvious canopy characteristics. The DSM segmentation algorithm [14] uses the slope characteristics of the canopies to segment the conifers and even the mixed forest with weak canopy characteristics. However, it is easy to treat branches as individual trees, resulting in over-segmentation errors. The minimization energy function segmentation algorithm [15] can be applied to a variety of different forest types, but there are a huge number of parameters that need to be manually adjusted; therefore, the universality is poor. The adaptive mean shift algorithm [16] estimates the average canopy width of the whole forest land through the slope feature and uses it as the parameter of the mean shift algorithm to delineate the individual trees. This method is suitable for plantations with similar crown widths but performs poorly for mixed forests with large differences in crown width. In general, the universality and accuracy of the traditional top-down segmentation algorithm need to be improved when segmenting different kinds of forest lands [12; 15]. Benefiting from the strong robustness, accuracy, and universality of deep learning, more and more scholars have begun to adopt deep learning methods to ITS [17]. However, due to the interweaving and complexity in high-canopy-density forests, the deep learning ITS methods still have under-segmentation and over-segmentation [18; 19; 20]. In recent years, point cloud deep learning has been widely used in point cloud segmentation tasks due to its strong universality and accuracy. [PERSON] et al. [21] proposed PointNet, which can directly segment point clouds without additional transformation but does not consider the relationship among points, which makes it difficult to analyze the intricate forest structure.