Performance of unsupervised machine learning methods using chi-squared weights for LiDAR point cloud filtering in urban areas

ŞEN A., SÜLEYMANOĞLU B., SOYCAN M.

JOURNAL OF SPATIAL SCIENCE, vol.68, no.3, pp.397-414, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 68 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1080/14498596.2021.2013329
  • Journal Name: JOURNAL OF SPATIAL SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Geobase, INSPEC
  • Page Numbers: pp.397-414
  • Keywords: Airborne LiDAR, point cloud filtering, K-means, linkage, SOM, PROGRESSIVE TIN DENSIFICATION, MORPHOLOGICAL FILTER, EXTRACTION, ALGORITHMS, CLASSIFICATION
  • Yıldız Technical University Affiliated: Yes

Abstract

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.