The Effect of Point Density on Point Cloud Filtering Performance
Öz
Point cloud filtering is an important step in Digital Terrain Model (DTM) production. Despite the fact that a great body of research has been conducted in this area so far, there are still some problems that have not yet been solved, especially in complex terrains. The fact that the use of user-defined parameters within the presented point cloud filtering methods, and the difficulty of parameter estimation in parallel to the increase in the topography slope and above-ground object diversity, decreases the filtering success. Another problem is the proper specification of the point cloud density to be studied. Point cloud density, which is generally specified considering the ground sampling distance of the DTM, influences the success of the point cloud filtering process, therefore, the accuracy of the DTM produced. In this study, five Unmanned Aerial System (UAS)-based point clouds of different densities were filtered using two different point cloud filtering algorithms Cloth Simulation Filtering (CSF) and gLiDAR to examine the impacts of the point cloud density on filtering success. It was found that the point cloud filtering performance decreased as the point density increased.
Anahtar Kelimeler
Kaynakça
- Agisoft PhotoScan Professional user manual. Version 1.2. 2016. 14. Russia: Agisoft LLC.
- Ali-Sisto, D., & Packalen, P. (2017). Forest change detection by using point clouds from dense image matching together with a LiDAR-derived terrain model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3), 1197-1206. doi: 10.1109/JSTARS.2016.2615099.
- Arrighi, C., & Campo, L. (2019). Effects of digital terrain model uncertainties on high‐resolution urban flood damage assessment. Journal of Flood Risk Management, 12(S2), e12530. doi: 10.1111/jfr3.12530.
- Boiarskii, B., Hasegawa, H., Muratov, A., & Sudeykin, V. (2019). Application of UAV-derived digital elevation model in agricultural field to determine waterlogged soil areas in Amur region, Russia. International Journal of Engineering and Advanced Technology, 8, 520-523.
- Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
- Demir, N. (2018). Using UAVs for detection of trees from digital surface models. Journal of Forestry Research, 29(3), 813-821. doi: 10.1007/s11676-017-0473-9.
- Douass, S., and Ait Kbir, M. (2020). Flood zones detection using a runoff model built on Hexagonal shape based cellular automata. International Journal of Engineering Trends and Technology (IJETT), 68(6), 68-74.
- Karakas, G., Can, R., Kocaman, S., Nefeslioglu, H. A., & Gokceoglu, C. (2020). Landslide susceptibility mapping with random forest model for Ordu, Turkey. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1229-1236. doi: 10.5194/isprs-archives-XLIII-B3-2020-1229-2020.
- Korzeniowska, K., Pfeifer, N., Mandlburger, G., & Lugmayr, A. (2014). Experimental evaluation of ALS point cloud ground extraction tools over different terrain slope and land-cover types. International Journal of Remote Sensing, 35(13), 4673-4697. doi: 10.1080/01431161.2014.919684.
- Mongus, D., & Žalik, B. (2012). Parameter-free ground filtering of LiDAR data for automatic DTM generation. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 1-12. doi: 10.1016/j.isprsjprs.2011.10.002.
