Makale detayı · 2023
Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas
- Yıl
- 2023
- ISSN
1449-8596- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
İngilizce (OpenAlex)
In this study, we compared the LiDAR filtering performances of unsupervised machine learning methods, such as linkage, K-means, and self-organizing maps, for urban areas to provide a practical guide to researchers. The input parameters (x-y-z and intensity) were normalized and weighted using a chi-squared independence test to improve the classification accuracy. The best successful results were obtained using the weighted linkage method in terms of the total error of 13.53%, 3.96%, and 1.07% for the three samples, respectively. In comparison with other approaches, methods weighted by chi-squared have significant potential for classification and filtering and outperform many popular approaches.
Konular
- Remote Sensing and LiDAR Applications
- Remote Sensing in Agriculture
- 3D Surveying and Cultural Heritage
Birincil konu Remote Sensing and LiDAR Applications