基于Noise4Denoise的LiDAR点云对单个树木的提取进行无声化.
Xiangfei Lu1, Zongyu Ye2, Liyong Fu3
1Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing, China.
Frontiers in plant science
|January 24, 2025
概括
DEN4是一种新的深度学习算法,有效地消除了LiDAR点云,以改善森林调查. 它提高了单一树的细分精度,在各种指标上优于传统方法,而不需要预先标记的数据.
科学领域:
- 林业和远程传感 林业和远程传感
- 计算机视觉和机器学习
- 地理空间数据分析.
背景情况:
- 对于森林资源调查和生态研究来说,LiDAR点云数据处理至关重要.
- 传统的denoising算法在不同数据集的适应性和有效性方面扎.
- 准确的点云消噪对于可靠的单一树分段至关重要.
研究的目的:
- 介绍DEN4,一个无监督的深度学习算法,用于LiDAR点云消除噪音.
- 为了提高单个树段的准确性,使用denoised点云.
- 为了评估DEN4的性能与传统的无雾化方法相比.
主要方法:
- DEN4使用多层噪声分离模块来区分信号和噪声.
- 该算法以无监督的方式运行,不需要预先标记的数据.
- 使用诸如平均平方误差 (MSE),信号与噪声比 (SNR),豪斯多夫距离和结构相似性指数 (SSIM) 等指标来评估性能.
主要成果:
- 在MSE,SNR,豪斯多夫距离和SSIM中,DEN4显著优于传统的无声化方法.
- 在60个样本的数据集中,DEN4在所有评估指标中实现了优越的平均值和标准偏差.
- 在S10数据集中,与PTD相比,DEN4减少了MSE的70.2%,并增加了SNR的37.8%.
结论:
- DEN4在各种森林数据集中表现出高效率,几何完整性和稳定性.
- 无监督的性质和强大的性能使得DEN4适合单一树的细分.
- DEN4为先进的森林资源管理和生态研究提供了一个有前途的解决方案.
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