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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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L. Truong-Hong, F. Jia, E. Che, S. Emamgholian, D. Laefer, and A. V. Vo
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A. V. Vo, C. N. Lokugam Hewage, N. A. Le Khac, M. Bertolotto, and D. Laefer
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L. Truong-Hong, N. Nguyen, R. Lindenbergh, P. Fisk, and T. Huynh
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G. S. Agostini, Y. Zhang, and D. F. Laefer
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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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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 155–161, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-155-2019, 2019
D. Backes, G. Schumann, F. N. Teferle, and J. Boehm
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 181–187, https://doi.org/10.5194/isprs-archives-XLII-2-W13-181-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-181-2019, 2019
K. Anders, R. C. Lindenbergh, S. E. Vos, H. Mara, S. de Vries, and B. Höfle
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 317–324, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-317-2019, 2019
M. Soilán, R. Lindenbergh, B. Riveiro, and A. Sánchez-Rodríguez
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 445–452, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-445-2019, 2019
A. V. Vo, N. Chauhan, D. F. Laefer, and M. Bertolotto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 671–678, https://doi.org/10.5194/isprs-archives-XLII-4-671-2018, https://doi.org/10.5194/isprs-archives-XLII-4-671-2018, 2018
R. P. A. Bormans, R. C. Lindenbergh, and F. Karimi Nejadasl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 141–148, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, https://doi.org/10.5194/isprs-archives-XLII-2-141-2018, 2018
I. Puente, R. Lindenbergh, A. Van Natijne, R. Esposito, and R. Schipper
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 923–929, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, https://doi.org/10.5194/isprs-archives-XLII-2-923-2018, 2018
B. Riveiro, G. Cubreiro, B. Conde, M. Cabaleiro, R. Lindenbergh, M. Soilán, and J. C. Caamaño
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 969–974, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, https://doi.org/10.5194/isprs-archives-XLII-2-969-2018, 2018
A. L. van Natijne, R. C. Lindenbergh, and R. F. Hanssen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1137–1144, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1137-2018, 2018
J. Wang and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163–1168, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018, 2018
E. Widyaningrum, R. C. Lindenbergh, B. G. H. Gorte, and K. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199–1205, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018, 2018
K. Zhou, B. Gorte, R. Lindenbergh, and E. Widyaningrum
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1229–1235, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1229-2018, 2018
Y. Shen, R. Lindenbergh, B. Hofland, and R. Kramer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 139–147, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-139-2017, 2017
A. Nurunnabi, Y. Sadahiro, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 63–70, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-63-2017, 2017
Anna Klos, Addisu Hunegnaw, Felix Norman Teferle, Kibrom Ebuy Abraha, Furqan Ahmed, and Janusz Bogusz
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-385, https://doi.org/10.5194/amt-2016-385, 2016
Revised manuscript not accepted
Short summary
Short summary
GPS can remotely sense integrated atmospheric water vapour and in-doing so improve e.g. the accuracy of assimilated numerical weather models. Specially, GPS is well suited in the study of the atmospheric conditions since it is increasingly deployed ever widely around the globe. In this research, we used trend estimates of Zenith Total Delay series and provide a most recent picture of trend and their uncertainties which are used widely to interpret a change in climate.
Tzvetan Simeonov, Dmitry Sidorov, Felix Norman Teferle, Georgi Milev, and Guergana Guerova
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-152, https://doi.org/10.5194/amt-2016-152, 2016
Revised manuscript not accepted
J. Böhm, M. Bredif, T. Gierlinger, M. Krämer, R. Lindenberg, K. Liu, F. Michel, and B. Sirmacek
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 301–307, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016, 2016
V. H. Phan, R. C. Lindenbergh, and M. Menenti
The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-2425-2014, https://doi.org/10.5194/tcd-8-2425-2014, 2014
Revised manuscript not accepted
J. F. Levinsen, K. Khvorostovsky, F. Ticconi, A. Shepherd, R. Forsberg, L. S. Sørensen, A. Muir, N. Pie, D. Felikson, T. Flament, R. Hurkmans, G. Moholdt, B. Gunter, R. C. Lindenbergh, and M. Kleinherenbrink
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-5433-2013, https://doi.org/10.5194/tcd-7-5433-2013, 2013
Revised manuscript not accepted
V. H. Phan, R. C. Lindenbergh, and M. Menenti
Hydrol. Earth Syst. Sci., 17, 4061–4077, https://doi.org/10.5194/hess-17-4061-2013, https://doi.org/10.5194/hess-17-4061-2013, 2013
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