Integrating topographic knowledge into point cloud simplification for terrain modelling

References

• Alexa, M., et al., 2003. Computing and rendering point set surfaces. IEEE Transactions on Visualization and Computer Graphics, 9 (1), 3–15.
   Web of Science ®Google Scholar
• Alsabti, K., et al., 1997. An efficient k-means clustering algorithm. Electrical Engineering and Computer Science, 43.
   Google Scholar
• Anwer, N., and Mathieu, L., 2016. From reverse engineering to shape engineering in mechanical design. CIRP Annals, 65 (1), 165–168.
   Web of Science ®Google Scholar
• Blickle, T., and Thiele, L., 1996. A comparison of selection schemes used in evolutionary algorithms. Evolutionary Computation, 4 (4), 361–394.
   Web of Science ®Google Scholar
• Chen, N., and Lu, X., 2021. A novel point cloud simplification method with integration of multiple-feature fusion and density uniformity. Measurement Science and Technology, 32 (12), 125211.
   Web of Science ®Google Scholar
• Chen, Y., and Zhou, Q., 2013. A scale-adaptive DEM for multi-scale terrain analysis. International Journal of Geographical Information Science, 27 (7), 1329–1348.
   Web of Science ®Google Scholar
• Demantké, J., et al., 2012. Dimension-based scaling selection in 3D LiDAR point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W12, 97–102.
   Google Scholar
• Deng, B., et al., 2017. Multi-pose system for geometric measurement of large-scale assembled rotational parts. Measurement Science and Technology, 28 (5), 055011.
   Web of Science ®Google Scholar
• Diakoulaki, D., et al., 1995. Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22 (7), 763–770.
   Web of Science ®Google Scholar
• Dong, G., et al., 2020. A shadow constrained conditional generative adversarial net for SRTM data restoration. Remote Sensing of Environment, 237, 111602.
   Web of Science ®Google Scholar
• Dong, Y.F., and Tang, G.A., 2013. Research on terrain simplification using terrain significance information index from digital elevation models. Geomatics and Information Science of Wuhan University, 38 (03), 353–357.
   Google Scholar
• Evans, I.S., 2012. Geomorphometry and landform mapping: What is a landform? Geomorphology, 137 (1), 94–106.
   Web of Science ®Google Scholar
• Jenks, G.F., 1967. The data model concept in statistical mapping. International Yearbook of Cartography, 7, 186–190.
   Google Scholar
• Gaulton, R., and Malthus, T.J., 2010. LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques. International Journal of Remote Sensing, 31 (5), 1193–1211.
   Web of Science ®Google Scholar
• Guilbert, E., and Moulin, B., 2017. Towards a common framework for the identification of landforms on terrain models. ISPRS International Journal of Geo-Information, 6 (1), 12.
   Web of Science ®Google Scholar
• Hu, G.H., et al., 2020. A vector operation to extract second-order terrain derivatives from digital elevation models. Remote Sensing, 12 (19), 3134.
   Web of Science ®Google Scholar
• Hu, G.H., et al., 2022. Mathematical vector framework for gravity-specific land surface curvatures calculation from triangulated irregular networks. GIScience & Remote Sensing, 59 (1), 590–608.
   Web of Science ®Google Scholar
• Leal, N., et al., 2017. A linear programming approach for 3D point cloud simplification. IAENG International Journal of Computer Science, 44 (1), 8.
   Google Scholar
• Lee, K., et al., 2001. Data reduction methods for reverse engineering. The International Journal of Advanced Manufacturing Technology, 17 (10), 735–743.
   Web of Science ®Google Scholar
• Levin, D., 2004. Mesh-independent surface interpolation. In: G. Brunnett, B. Hamann, H. Müller, and L. Linsen, eds. Geometric modeling for scientific visualization. Cham: Springer, 37–49.
   Google Scholar
• Li, Q., et al., 2018. Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm. Measurement Science and Technology, 29 (11), 115202.
   Web of Science ®Google Scholar
• Li, S., et al., 2022. Integrating topographic knowledge into deep learning for the void-filling of digital elevation models. Remote Sensing of Environment, 269, 112818.
   Web of Science ®Google Scholar
• Li, X., et al., 2021. Airborne LiDAR: state-of-the-art of system design, technology and application. Measurement Science and Technology, 32 (3), 032002.
   Web of Science ®Google Scholar
• Liu, J., 2008. Cultural relic 3D reconstruction from digital images and laser point clouds. 2008 Congress on Image and Signal Processing, 2, 349–353.
   Google Scholar
• Luebke, D.P., 2001. A developer’s survey of polygonal simplification algorithms. IEEE Computer Graphics and Applications, 21 (1), 24–35.
   Google Scholar
• Lv, G., et al., 2017. Chinese progress in geomorphometry. Journal of Geographical Sciences, 27 (11), 1389–1412.
   Web of Science ®Google Scholar
• Lyu, F., et al., 2021. A vector-based method for drainage network analysis based on LiDAR data. Computers & Geosciences, 156, 104892.
   Web of Science ®Google Scholar
• Mahdaoui, A., and Sbai, E.H., 2020. 3D point cloud simplification based on k-nearest neighbor and clustering. Advances in Multimedia, 2020, 1–10.
   Web of Science ®Google Scholar
• Mark, D.M., 1975. Geomorphometric parameters: a review and evaluation. Geografiska Annaler: Series A, Physical Geography, 57 (3–4), 165–177.
   Google Scholar
• Maurya, R.K., et al., 2018. Morphology-based extraction of river network from multidimensional topographic LiDAR returns. 2018 3rd International Conference for Convergence in Technology (I2CT), 1–6.
   Google Scholar
• Minár, J., et al., 2020. A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction. Earth-Science Reviews, 211, 103414.
   Web of Science ®Google Scholar
• Peng, J., et al., 2005. Technologies for 3D mesh compression: A survey. Journal of Visual Communication and Image Representation, 16 (6), 688–733.
   Web of Science ®Google Scholar
• Pieczonka, T., et al., 2011. Generation and evaluation of multitemporal digital terrain models of the Mt. Everest area from different optical sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (6), 927–940.
   Web of Science ®Google Scholar
• Podobnikar, T., and Vrečko, A., 2012. Digital elevation model from the best results of different filtering of a LiDAR point cloud: high quality DEM from LiDAR data. Transactions in GIS, 16 (5), 603–617.
   Web of Science ®Google Scholar
• Polidori, L., and Chorowicz, J., 1993. Comparison of bilinear and Brownian interpolation for digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing, 48 (2), 18–23.
   Web of Science ®Google Scholar
• Reuter, H.I., et al., 2007. An evaluation of void‐filling interpolation methods for SRTM data. International Journal of Geographical Information Science, 21 (9), 983–1008.
   Web of Science ®Google Scholar
• Shary, P.A., 1995. Land surface in gravity points classification by a complete system of curvatures. Mathematical Geology, 27 (3), 373–390.
   Google Scholar
• Shi, B.Q., et al., 2011. Adaptive simplification of point cloud using k-means clustering. Computer-Aided Design, 43 (8), 910–922.
   Web of Science ®Google Scholar
• Sharpnack, D.A., and Akin, G., 1969. An algorithm for computing slope and aspect from elevations. Photogrammetric Engineering, 35 (3), 247–248.
   Web of Science ®Google Scholar
• Song, H., and Feng, H.Y., 2008. A global clustering approach to point cloud simplification with a specified data reduction ratio. Computer-Aided Design, 40 (3), 281–292.
   Web of Science ®Google Scholar
• Wang, G., et al., 2021. Point cloud simplification algorithm based on the feature of adaptive curvature entropy. Measurement Science and Technology, 32 (6), 065004.
   Web of Science ®Google Scholar
• Wang, Q., et al., 2016. Analysis of gully erosion hazard using high resolution terrestrial LiDAR. IEEE International Geoscience and Remote Sensing Symposium, 7469–7472.
   Google Scholar
• Xiong, L.Y., et al., 2014. Modeling the evolution of loess-covered landforms in the Loess Plateau of China using a DEM of underground bedrock surface. Geomorphology, 209, 18–26.
   Web of Science ®Google Scholar
• Xiong, L.Y., et al., 2017. A peak-cluster assessment method for the identification of upland planation surfaces. International Journal of Geographical Information Science, 31 (2), 387–404.
   Web of Science ®Google Scholar
• Xiong, L.Y., et al., 2022. Geomorphometry and terrain analysis: data, methods, platforms and applications. Earth-Science Reviews, 233, 104191.
   Web of Science ®Google Scholar
• Xiong, L.Y., et al., 2023. Past rainfall-driven erosion on the Chinese loess plateau inferred from archaeological evidence from Wucheng City, Shanxi. Communications Earth & Environment, 4 (1), 4.
   Google Scholar
• Xu, W., et al., 2022. Feature curve-based registration for airborne LiDAR bathymetry point clouds. International Journal of Applied Earth Observation and Geoinformation, 112, 102883.
   Web of Science ®Google Scholar
• Xuan, W., et al., 2018. A new progressive simplification method for point cloud using local entropy of normal angle. Journal of the Indian Society of Remote Sensing, 46 (4), 581–589.
   Web of Science ®Google Scholar
• Yang, B., and Li, J., 2022. A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 403–421.
   Web of Science ®Google Scholar
• Yang, Y., et al., 2020. A point cloud simplification method based on modified fuzzy c-means clustering algorithm with feature information reserved. Mathematical Problems in Engineering, 2020, 1–13.
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2022. Terrain feature-aware deep learning network for digital elevation model superresolution. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 143–162.
   Web of Science ®Google Scholar
• Zhao, P., et al., 2016. A feature preserving algorithm for point cloud simplification based on hierarchical clustering. IEEE International Geoscience and Remote Sensing Symposium, 5581–5584.
   Google Scholar
• Zhao, W., et al., 2019. A practical method for reducing terrain effect on land surface temperature using random forest regression. Remote Sensing of Environment, 221, 635–649.
   Web of Science ®Google Scholar
• Zhou, W., et al., 2018. Automated extraction of 3D vector topographic feature line from terrain point cloud. Geocarto International, 33 (10), 1036–1047.
   Web of Science ®Google Scholar