点云
计算机科学
激光雷达
人工智能
测距
塔架
分类器(UML)
支持向量机
特征提取
计算机视觉
遥感
模式识别(心理学)
随机森林
阿达布思
地理
电信
考古
作者
Junjun Huang,Yiping Shen,Jinguo Wang,Vagner G. Ferreira
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-11
被引量:2
标识
DOI:10.1109/tim.2023.3293543
摘要
Extracting pylons from point cloud gathered by Unmanned Aerial Vehicle LiDAR systems (UAVLS) is challenging, particularly in complex environments. The difficulty arises due to the presence of nearby objects and vegetation, which are intertwined around the pylon legs and structures. In such situations, it is hard to identify pylon points from vegetation points using spatial point-level features. To overcome this issue, we propose a classification approach using the Random Forest classifier incorporating smoothed color features. The procedure involves the following steps: first, a set of local geometry and distribution features are generated by combining spherical and cylindrical neighborhoods. The optimal neighborhood radius is then determined by experimenting with radii ranging from 1 to 8m. To further enhance the method’s accuracy, we introduce smoothed color features within a neighborhood to eliminate the misclassification points of the vegetation close to the pylon leg. The results indicate that smoothed color features outperform intensity and single-color features, achieving a high extraction rate of 98.65% and 99.99% for the two datasets, respectively. Furthermore, our method is robust against varying levels of noise and density, and it significantly improves pylon classification accuracy when coupled with other classifiers such as SVM and AdaBoost.
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