在森林地区使用光检测和范围点云的深度顺序分类
Alejandro Morales-Martín1, Francisco-Javier Mesas-Carrascosa2, Pedro Antonio Gutiérrez1
1Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, 14071 Córdoba, Spain.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种新的软标签技术,用于分类森林光检测和测距 (LiDAR) 数据. 该方法提高了3D点云分类的准确性,特别是用于区分植被类型.
科学领域:
- 地理空间科学 地理空间科学
- 计算机科学 计算机科学
- 林业林业 林业 林业 林业
背景情况:
- 深度学习和空中光检测和测距 (LiDAR) 推进了环境监测的3D点云分析.
- 森林结构的准确分类对于生态研究和管理至关重要.
研究的目的:
- 为森林地区的LiDAR点云开发一个顺序分类模型.
- 将点云分为四个不同的类别:地面,低,中和高植被.
- 通过使用一种新的软标签技术来增强PointNet网络架构.
主要方法:
- 应用一种新的泛指数函数 (CE-GE) 用于软标签.
- 使用PointNet深度学习架构.
- 使用科尔莫戈罗夫-斯米尔诺夫和学生的t测试进行统计验证.
主要成果:
- 与现有方法相比,CE-GE方法在所有评估指标上都表现出优异的表现.
- 使用光滑标签的顺序分类导致比名义分类更一致的结果.
- 在点对点分类准确度的显著提高,特别是在区分低和中等植被.
结论:
- 拟议的CE-GE软标签技术有效地完善了森林LiDAR点云的分类.
- 该方法提高了像PointNet这样的深度学习模型的准确性,用于复杂的环境监测.
- 这种方法为在3D点云数据中区分植被层提供了更强大的解决方案.
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