Abstract
LiDAR technology can be used to carry out ground extraction with higher resolutions. The technique can penetrate the topping of vegetation and collect ground points for bare land estimation. But for thick forest, the simple selection of return echoes to generate ground is not accurate. To solve the problem, this paper presents a progressive framework to segment ground out of densely-vegetated area from LiDAR data. A morphological filter is applied to the process by a multi-scale representation which can adaptively respond to different environment with the parameter of the structure element is progressively increased by one pixel from the starting value at each step. The proposed method also incorporates image inpainting in the implementation of a smooth initial data grid and missing point data restoration. After all, the performances of the framework are tested against different data of densely-vegetated and sloped terrain, and yield a better result.
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References
Glennie, C.L., Carter, W.E., Shrestha, R.L., Dietrich, W.E.: Geodetic imaging with airborne LiDAR: the Earth’s surface revealed. Rep. Prog. Phys. 76, 086801 (2013)
Bertalmio, M., Guillermo, S., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 2000), New Orleans, LA, July 23-28, pp. 417–424 (2000)
Cho, H.-C., Kampa, K., Slatton, K.C.: Morphological segmentation of Lidar Digital Elevation Models to extract stream channels in forested terrain. In: IGARSS, July 23-28, pp. 3182–3185 (2007)
Sithole, G., Vosselmann, G.: Experimental comparison of filter algorithms for bare earth extraction from airborne laser scanning point clouds. ISPRS J. Photogramm. Remote Sens. 59, 85–101 (2004)
Meng, X., Currit, N., Zhao, K.: Ground filtering algorithms for airborne LiDAR data: a review of critical issues. Remote Sens. 2, 833–860 (2010)
Zhang, K., Chen, S., Whitman, D., Yan, J., Shu, M., Zhang, C.: A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Trans. Geosci. Remot. Sen. 41(4), 872–882 (2003)
G-LiHT team at Goddard Space Flight Center, NASA, http://gliht.gsfc.nasa.gov
Vegetation Monitoring and Remote Sensing Team of Pacific Northwest Research Station, USDA Forest Service, http://forsys.cfr.washington.edu/fusion/fusionlatest.html
Kraus, K., Pfeifer, N.: Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 53(4), 193–203 (1998)
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Liu, L., Shao, Z. (2013). A Multi-scale Progressive Framework for Ground Segmentation of Airborne LiDAR Data. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_41
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DOI: https://doi.org/10.1007/978-3-642-45025-9_41
Publisher Name: Springer, Berlin, Heidelberg
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