几何特征分析对深度学习的贡献 城市LiDAR数据的分类算法
Fayez Tarsha Kurdi1, Wijdan Amakhchan2, Zahra Gharineiat1
1School of Surveying and Built Environment, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Springfield Campus, Springfield, QLD 4300, Australia.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
这项研究提高了使用深度学习管道网络 (DLPN) 的空中城市光检测和距离 (LiDAR) 点云分类. 开发的算法在识别建筑物,地形和植被方面达到高准确度 (89-98%).
科学领域:
- 地理空间分析是什么?
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 空载LiDAR点云分类对于城市绘图至关重要.
- 现有的机器学习 (ML) 方法需要增强,以提高准确性.
- 深度学习 (DL) 为更有效的分类提供了潜力.
研究的目的:
- 评估提高空载城市立达点云DL分类的策略.
- 使用DL管道网络 (DLPN) 开发和比较两种ML分类方法.
- 为了评估DL算法的性能在不同的城市数据集.
主要方法:
- 基于点及其邻近的几何属性进行特征选择.
- 实施两种ML分类方法,使用定制的DLPN.
- 在来自无人机和飞机来源的五个不同的LiDAR点云数据集上测试算法.
主要成果:
- 获得了89%至98%的高分类准确度.
- 基于DLPN的方法在分类城市特征方面表现出显著的有效性.
- 该算法在具有不同点密度和地形的数据集中表现良好.
结论:
- 开发的DL算法有效地对空中城市LiDAR点云进行了分类.
- 与传统方法相比,DLPN策略提高了分类准确性.
- 这项研究为DL在LiDAR数据处理中的性能提供了有价值的见解.
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