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Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-24 , DOI: 10.1016/j.jag.2022.103028
Zhenyang Hui, Penggen Cheng, Bisheng Yang, Guoqing Zhou

To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods.



中文翻译:

基于机载激光雷达的针叶林多级自适应单树检测

为了从 LiDAR 点中获得令人满意的单棵树检测结果,使用传统方法的参数通常需要通过反复试验来调整。在遇到复杂的森林环境时,无法满足检测精度。为了解决这个问题,本文提出了一种多层次的自适应个体树检测方法。所提出的方法可以看作是一种混合模型,它结合了基于栅格和基于点的方法的优势。首先使用基于栅格的策略来获得初始树木检测结果,而基于点的策略用于优化聚类树。在所提出的方法中,冠宽尺度是自动估计的。同时,将多尺度分割结果融合在一起,以利用大尺度和小尺度的分割结果。采用六种不同的针叶林进行测试。实验结果表明,与其他三种经典方法相比,本研究实现了最低的遗漏和佣金错误。同时,本文的平均 F1 分数为 0.84,远高于其他方法。

更新日期:2022-09-25
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