Comparison of Data Reduction Algorithms for Image-Based Point Cloud Derived Digital Terrain Models

Authors: M. Uysal, M. Yilmaz, I. Tiryakioğlu

Abstract:

Digital Terrain Model (DTM) is a digital numerical representation of the Earth's surface. DTMs have been applied to a diverse field of tasks, such as urban planning, military, glacier mapping, disaster management. In the expression of the Earth' surface as a mathematical model, an infinite number of point measurements are needed. Because of the impossibility of this case, the points at regular intervals are measured to characterize the Earth's surface and DTM of the Earth is generated. Hitherto, the classical measurement techniques and photogrammetry method have widespread use in the construction of DTM. At present, RADAR, LiDAR, and stereo satellite images are also used for the construction of DTM. In recent years, especially because of its superiorities, Airborne Light Detection and Ranging (LiDAR) has an increased use in DTM applications. A 3D point cloud is created with LiDAR technology by obtaining numerous point data. However recently, by the development in image mapping methods, the use of unmanned aerial vehicles (UAV) for photogrammetric data acquisition has increased DTM generation from image-based point cloud. The accuracy of the DTM depends on various factors such as data collection method, the distribution of elevation points, the point density, properties of the surface and interpolation methods. In this study, the random data reduction method is compared for DTMs generated from image based point cloud data. The original image based point cloud data set (100%) is reduced to a series of subsets by using random algorithm, representing the 75, 50, 25 and 5% of the original image based point cloud data set. Over the ANS campus of Afyon Kocatepe University as the test area, DTM constructed from the original image based point cloud data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that the random data reduction method can be used to reduce the image based point cloud datasets to 50% density level while still maintaining the quality of DTM.

Keywords: DTM, unmanned aerial vehicle, UAV, random, Kriging.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2702570

References:

[1] Li, Z., Zhu, C., Gold, C. Digital Terrain Modeling: Principles and Methodology. (2005). Boca Raton: CRC Press.
[2] Yılmaz, M., Uysal, M., A Comparative Study Of Curvature And Grid Data Reduction Algorithms For Lidar-Derived Digital Terrain Models. Proceedings, 6 th International Conference on Cartography and GIS, 13-17 June 2016, Albena, Bulgaria
[3] Ma, R., Meyer, W. DTM generation and building detection from LiDAR data. Photogrammetric Engineering and Remote Sensing, (2005). 71, 847-854.
[4] Liu, X., Zhang, Z. LiDAR data reduction for efficient and high quality DEM generation, The International Archives of the Photogrammtery, Remote Sensing and Spatial Information Sciences, (2008). 3, XXXVII, 173-178.
[5] Vianello, A., Cavalli, M., Tarolli, P. LiDAR-derived slopes for headwater channel network analysis. Catena, (2009). 76 (2), 97-106.
[6] Razak, K.A., Straatsma, M.W., van Westen, C.J., Malet, J.P., de Jong, S.M. Airborne laser scanning of forested landslides characterization: Terrain model quality and visualization. Geomorphology, (2011). 126, 186-200.
[7] Yan, W.Y., Shaker, A., El-Ashmawy, N. Urban land cover classification using airborne LiDAR data: A review. Remote Sensing of Environment, (2015). 158, 295-310.
[8] Polat, N., Uysal, M. DTM Generation with Uav Based Photogrammetric Point Cloud. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2017). Volume XLII-4/W6, 2017, pp.77-79.
[9] Eisenbeiss, H., Lambers, K., Sauerbier, M., & Li, Z. Photogrammetric documentation of an archaeological site (Palpa, Peru) using an autonomous model helicopter. (2005). In Proceedings of International CIPA Symposium (pp. 238–243).
[10] Colomina, I., Bla´zquez, M., Molina, P., Pare´s, M. E., & Wis, M. Towards a new paradigm for high-resolution low-cost photogrammetry and remote sensing. (2008). In Proceedings of XXIst ISPRS congress: Technical commission I (pp. 1201).
[11] Remondino, F., Barazzetti, L., Nex, F., Scaioni, M., & Sarazzi, D. U.AV photogrammetry for mapping and 3d modeling–current status and future perspectives. (2011). In Proceedings of the international archives of the photogrammetry, remote sensing and spatial information sciences, Volume XXXVIII-1/C22 (pp 25–31).
[12] Uysal, M. Toprak, A.S.; Polat, N. DEM generation with UAV Photogrammetry and accuracy analysis in Sahitlerhill. (2015). Measurement 2015, 73, 539–543.
[13] Polat, N., Uysal, M., An Experimental Analysis of Digital Elevation Models Generated with Lidar Data and UAV Photogrammetry. (2018). Journal of the Indian Society of Remote Sensing 46(7):1135–1142
[14] Gong, J., Li, Z., Zhu, Q., Sui, H., Zhou, Y. Effects of various factors on the accuracy of DEMs: an intensive experimental investigation. Photogrammetric Engineering and Remote Sensing, (2000). 66 (9), 1113-1117.
[15] Chen, C.F., Yue, T.X. A method of DEM construction and related error analysis. Computers and Geosciences, (2010).36 (6), 717-725.
[16] Sailer, R., Rutzinger, M., Rieg, L. Wichmann, V. Digital elevation models derived from airborne laser scanning point clouds: appropriate spatial resolutions for multi-temporal characterization and quantification of geomorphological processes. Earth Surface Processes and Landforms, (2014). 39 (2), 272-284.
[17] Rayburg, S., Thoms, M., Neave, M. A comparison of digital elevation models generated from different data sources. Geomorphology, (2009). 106, 261-270.
[18] Dorn, H., Vetter, M., Höfle, B. GIS-based roughness derivation for flood simulations: a comparison of orthophotos, LiDAR and crowdsourced geodata. Remote Sensing, (2014). 6, 1739-1759.
[19] Aguilar, F.J., Agüera, F., Aguilar, M.A., Carvajal, F. Effects of terrain morphology, sampling density and interpolation methods on grid DEM accuracy. Photogrammetric Engineering and Remote Sensing, (2005). 71 (7), 805-816.
[20] Chaplot, V., Darboux, F., Bourennane, H., Leguédois, S., Silvera, N., Phachomphon, K. Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology, (2006). 77, 126-141.
[21] Yılmaz, M., Uysal, M., Comparing Uniform And Random Data Reduction Methods For DTM Accuracy. International Journal of Engineering and Geosciences (IJEG), (2017).Vol;2, Issue;01, pp. 9-16
[22] Yılmaz, M., Uysal, M., Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalization. Area. (2016). 48(4):521–532
[23] Chen, C., Li, Y. A robust method of thin plate spline and its application to DEM construction. Computers and Geosciences, (2012). 48, 9-16.
[24] Arun, P.V. A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Sciences, (2013). 16, 133-139.
[25] Aguilar, F.J., Aguilar, M.A., Agüera, F. Accuracy assessment of digital elevation models using a non-parametric approach. International Journal of Geographical Information Science, (2007). 21 (6), 66-686.
[26] Chu, H.J., Chen, R.A., Tseng, Y.H., Wang, C.K. Identifying LiDAR sample uncertainty on terrain features from DEM simulation, Geomorphology (2014).204, 325-333.
[27] Siebert, S. Teizer, J. Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system. Autom Constr. (2014). 1–14.
[28] Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. An easy-to-use airborne lidar data filtering method based on cloth simulation. Remote Sensing, (2016). 8(6).
[29] Anderson, E.S., Thompson, J.A., Austin, R.E. LIDAR density and linear interpolator effects on elevation estimates. International Journal of Remote Sensing, (2005). 26 (18), 3889-3900.
[30] Anderson, E.S., Thompson, J.A., Crouse, D.A., Austin, R.E. Horizontal resolution and data density effects on remotely sensed LIDAR-based DEM. Geoderma, (2006). 132 406– 415.