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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVI-2/W1-2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-401-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 Feb 2022
| 25 Feb 2022
A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION
A. Nurunnabi
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
F. N. Teferle
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
D. F. Laefer
Center for Urban Science and Progress and Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, USA
R. C. Lindenbergh
Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands
A. Hunegnaw
Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
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O. Majgaonkar, K. Panchal, D. Laefer, M. Stanley, and Y. Zaki
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W4-2021, 101–106, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-101-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-101-2021, 2021
L. Truong-Hong, N. Nguyen, R. Lindenbergh, P. Fisk, and T. Huynh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W4-2021, 119–124, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-119-2021, 2021
G. S. Agostini, Y. Zhang, and D. F. Laefer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W4-2021, 125–130, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-125-2021, https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-125-2021, 2021
A. Nurunnabi, F. N. Teferle, J. Li, R. C. Lindenbergh, and A. Hunegnaw
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 31–38, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-31-2021, 2021
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Short summary
Short summary
Sandy coasts are areas that undergo a lot of changes, which are caused by different influences, such as tides, wind or human activity. Permanent laser scanning is used to generate a three-dimensional representation of a part of the coast continuously over an extended period. By comparing three unsupervised learning algorithms, we develop a methodology to analyse the resulting data set and derive which processes are dominating changes in the beach and dunes.
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