The Effect of Point Density on Point Cloud Filtering Performance

Yıl 2021, , 41 - 46, 13.03.2021

Çiğdem Şerifoğlu Yılmaz , Oguz Güngör

https://doi.org/10.48123/rsgis.864234

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

Point cloud filtering, Digital terrain model, CSF, gLiDAR

Nokta Bulutu Yoğunluğunun Filtreleme Performansı Üzerine Etkisi

Öz

Nokta bulutu filtreleme sayısal arazi modeli üretiminde çok önemli bir aşamadır. Şimdiye kadar bu alanda pek çok çalışma yapılmıştır ancak, özellikle kompleks zeminlerde hala aşılamayan bazı sorunlar vardır. Sunulan yöntemlerde çoğunlukla kullanıcı girişli parametreler kullanılması ve parametre kestiriminin, topografya eğimi ve zemin üstü obje çeşitliliği arttıkça zorlaşması filtreleme başarısını düşürmektedir. Bir diğer sorun ise çalışılacak nokta bulutu yoğunluğunun uygun şekilde belirlenmesidir. Üretilecek sayısal arazi modelinin yer örnekleme aralığına göre belirlenen yoğunluk aynı zamanda nokta bulutunun filtreleme başarısını ve dolayısıyla elde edilecek sayısal arazi modelinin hassasiyetini de etkilemektedir. Bu çalışmada, 5 farklı yoğunlukta üretilen insansız hava aracı tabanlı nokta bulutları, nokta bulutu yoğunluğunun filtreleme başarısına etkilerini incelemek için Cloth Simulation Filtering (CSF) ve gLiDAR filtreleme algoritmaları kullanılarak filtrelenmiştir. Elde edilen sonuçlara göre nokta bulutu yoğunluğu arttıkça filtreleme başarısının düştüğü görülmüştür.

Anahtar Kelimeler

Nokta bulutu filtreleme, Sayısal arazi modeli, CSF, gLiDAR

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.
• Montealegre, A. L., Lamelas, M. T., & de la Riva, J. (2015). A comparison of open-source LiDAR filtering algorithms in a Mediterranean forest environment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8), 4072-4085. doi: 10.1109/JSTARS.2015.2436974.
• Serifoglu Yilmaz, C., & Gungor, O. (2018). Comparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud. Geocarto International, 33(5), 522-537. doi: 10.1080/10106049.2016.1265599.
• Serifoglu Yilmaz, C., Yilmaz, V., & Güngör, O. (2018). Investigating the performances of commercial and non-commercial software for ground filtering of UAV-based point clouds. International Journal of Remote Sensing, 39(15-16), 5016-5042. doi: 10.1080/01431161.2017.1420942.
• Serifoglu, C., Gungor, O., & Yilmaz, V. (2016). Performance Evaluation of Different Ground Filtering Algorithms for UAV-Based Point Clouds. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41. doi: 10.5194/isprsarchives-XLI-B1-245-2016.
• Sithole, G., & Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1-2), 85-101. doi: 10.1016/j.isprsjprs.2004.05.004.
• Yilmaz, V., & Güngör, O. (2019). Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds. International Journal of Remote Sensing, 40(2), 468-505. doi: 10.1080/01431161.2018.1562255.
• Zhang, K., & Whitman, D. (2005). Comparison of three algorithms for filtering airborne lidar data. Photogrammetric Engineering & Remote Sensing, 71(3), 313-324. doi: https://doi.org/10.14358/PERS.71.3.313.
• Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J., & Zhang, C. (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 872-882. doi: 10.1109/TGRS.2003.810682.
• Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. doi: 10.3390/rs8060501.