在林业应用中的LiDAR点云中改进了基于圆柱的树干检测
Shaobo Ma1, Yongkang Chen1, Zhefan Li1
1College of Resources and Environment, South China Agricultural University, Guangzhou 510642, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了随机样本共识圆柱体适配 (RANSAC-CyF),这是一个改进的算法,用于从LiDAR数据中检测单个树干. RANSAC-CyF提高了准确性和稳定性,特别是在复杂森林中的倾斜树木.
科学领域:
- 林业科学 林业科学
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
背景情况:
- 激光雷达技术对于森林调查至关重要,需要精确的单个树干检测来提取参数.
- 从LiDAR数据中检测树干的现有2D和3D算法在精度,空间信息丢失和复杂森林环境中的性能方面存在局限性.
- 目前的气装配方法中的倾斜树和敏感参数减少了在具有挑战性的条件下检测的成功.
研究的目的:
- 开发和验证一种改进的算法,即随机样本共识圆柱体装配 (RANSAC-CyF),用于从LiDAR点云中准确地检测单个树干.
- 解决现有方法的局限性,特别是关于空间信息丢失和对复杂森林环境参数的敏感性.
- 提高树干检测的稳定性和效率,用于林业调查和生态研究.
主要方法:
- 提出了随机样本共识圆柱体适配 (RANSAC-CyF) 算法,优化用于在3D LiDAR点云数据中检测圆柱形树干.
- 验证了RANSAC-CyF算法在广州天河区的三个不同复杂度的森林地块.
- 将RANSAC-CyF的性能与最小方形圆拟合 (LSCF) 和随机样本共识圆拟合 (RANSAC-CF) 算法进行了比较.
主要成果:
- 与LSCF和RANSAC-CF相比,RANSAC-CyF对树干的初始率显著更高 (平均差异为0.59,0.63,0.52与较低的率相比,p <0.05).
- 与其他算法 (26 和 40 个集群) 相比,在图表 1 和 2 中较少的集群 (2 和 8) 实现了 100% 的检测成功率.
- 在所有倾斜角度中表现出更广泛的有效距离值范围,并保持稳定的入门率 (>0.9),在具有挑战性的条件下超越了比较算法.
结论:
- RANSAC-CyF算法提供卓越的准确性,稳定性和适应性,用于在复杂的森林环境中使用LiDAR数据检测单个树干.
- 拟议的方法通过克服现有检测技术的局限性,显著提高了林业调查和生态研究的效率和精度.
- 即使在其他算法失败的地方,RANSAC-CyF即使在具有挑战性的图表中也表现出有效的性能,突出其实际适用性.
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