Abstract
Virtual 3D city modelling has several applications such as population estimation, navigation, visibility analysis. For accurate 3D city modelling, digital elevation model (DEM) data and digital terrain models (DTMs) are used as an important input. In order to estimate accurate height of building structures, ground filtering of DEM is required in order to obtain precise DTM. In this study, satellite-derived high resolutions (1 m) DEM data over different urban regions is used as an input and four different ground-filtering algorithms, i.e. thresholding-based ground filtering, morphological operation-based filtering, slope-based filtering, multidirectional ground-filtering (MGF) algorithm are implemented for filtering of DEM. Based on the detailed analysis and evaluations, It is concluded that the MGF algorithm outperforms other algorithms with an accuracy of > 99% over all the datasets.
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References
Beumier, C., & Idrissa, M. (2016). Digital terrain models derived from digital surface model uniform regions in urban areas. International Journal of Remote Sensing, 37(15), 3477–3493. https://doi.org/10.1080/01431161.2016.1182666
Canaz Sevgen, S., & Karsli, F. (2020). Automatic ground extraction for urban areas from airborne Lidar data. Turkish Journal of Engineering, 4(3), 113–122. https://doi.org/10.31127/tuje.641501
Chen, C., Guo, J., Wu, H., Li, Y., & Shi, B. (2021). Performance comparison of filtering algorithms for high-density airborne Lidar point clouds over complex landscapes. Remote Sensing, 13(14), 15. https://doi.org/10.3390/rs13142663
Gevaert, C. M., Persello, C., Nex, F., & Vosselman, G. (2018). A deep learning approach to DTM extraction from imagery using rule-based training labels. ISPRS Journal of Photogrammetry and Remote Sensing, 142(June), 106–123. https://doi.org/10.1016/j.isprsjprs.2018.06.001
Hu, X., & Yuan, Y. (2016). Deep-learning-based classification for DTM extraction from ALS point cloud. Remote Sensing, 8(9), 1–16. https://doi.org/10.3390/rs8090730
Jin, S., Su, Y., Zhao, X., Hu, T., & Guo, Q. (2020). A point-based fully convolutional neural network for airborne LiDAR ground point filtering in forested environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3958–3974. https://doi.org/10.1109/JSTARS.2020.3008477
Kim, K. H., & Shan, J. (2011). Adaptive morphological filtering for DEM generation. International Geoscience and Remote Sensing Symposium, 45, 2539–2542. https://doi.org/10.1109/IGARSS.2011.6049729
Li, Y., et al. (2021). A high-resolution satellite DEM filtering method assisted with building segmentation. Photogrammetric Engineering and Remote Sensing, 87(6), 421–430. https://doi.org/10.14358/PERS.87.6.421
Lin, X., & Zhang, J. (2014). Segmentation-based filtering of airborne LiDAR point clouds by progressive densification of terrain segments. Remote Sensing, 6(2), 1294–1326. https://doi.org/10.3390/rs6021294
Meng, X., Wang, L., Silván-cárdenas, J. L., & Currit, N. (2009). ISPRS Journal of Photogrammetry and Remote Sensing A multi-directional ground filtering algorithm for airborne LIDAR. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 117–124. https://doi.org/10.1016/j.isprsjprs.2008.09.001
Mousa, Y. A., Helmholz, P., & Belton, D. (2017). New DTM extraction approach from airborne images derived DSM. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII(June), 6–9. https://doi.org/10.5194/isprs-archives-XLII-1-W1-75-2017
Perko, R., Raggam, H., Gutjahr, K. H., & Schardt, M. (2015). Advanced DTM generation from very high resolution satellite stereo images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II(March), 25–27. https://doi.org/10.5194/isprsannals-II-3-W4-165-2015
Rashidi, P., & Rastiveis, H. (2017). Ground filtering LiDAR data based on multi-scale analysis of height difference threshold. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Archives), 2(4W4), 225–229. https://doi.org/10.5194/isprs-archives-XLII-4-W4-225-2017
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. https://doi.org/10.1080/10106049.2016.1265599
Singla, J., & Trivedi, S. (2022). 3D building reconstruction and validation using high-resolution stereo data. Current Science, 122(8), 900–906. https://doi.org/10.18520/cs/v122/i8/900-906
Vosselman, G. (2000). Slope based filtering of laser altimetry data. International Archives of the Photogrammetry, Remote Sensing, 33(Part B3/2), 678–684. https://doi.org/10.1016/S0924-2716(98)00009-4
Zhang, K., Chen, S., Whitman, D., Shyu, M., & Yan, J. (2003). A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(4), 872–882. https://doi.org/10.1109/TGRS.2003.810682
Acknowledgments
The authors express sincere gratitude to Shri. N. M. Desai, Director, Space Applications Centre for guiding and permitting the publication of this paper. Authors also acknowledge Smt Sunanda Trivedi, former head, AVTD for her constant support during this work. Suggestions from internal referees to improve an earlier version of this paper are sincerely acknowledged.
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Patel, H.B., Singla, J.G. Evaluation and Analysis of Ground Filtering Algorithms for Building Height Estimation on Satellite-Based High-Resolution DEM Data. J Indian Soc Remote Sens 51, 661–672 (2023). https://doi.org/10.1007/s12524-022-01659-9
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DOI: https://doi.org/10.1007/s12524-022-01659-9