Have a personal or library account? Click to login
Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors Cover

Aerial Point Cloud Classification Using an Alternative Approach for the Dynamic Computation of K-Nearest Neighbors

Open Access
|Nov 2020

References

  1. Ben-Joseph, E., Ishii, H., Underkoffler, J., Piper, B., Yeung, L., 2001. Urban simulation and the luminous planning table: Bridging the gap between the digital and the tangible. Journal of Planning Education and Research, 21 (2), pp. 196-203.10.1177/0739456X0102100207
  2. Breiman, L., 2001. Random forests. Machine learning, 45(1), pp. 5-32.10.1023/A:1010933404324
  3. Demantké, J., Mallet, C., David, N., Vallet, B., 2011. Dimensionality based scale selection in 3D lidar point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII-5/W12, pp. 97–102.10.5194/isprsarchives-XXXVIII-5-W12-97-2011
  4. Filin, S., Pfeifer, N., 2005. Neighborhood systems for airborne laser data. Photogrammetric Engineering and Remote Sensing, 71(6), pp. 743–755.10.14358/PERS.71.6.743
  5. Grilli, E., Özdemir, E., Remondino, F., 2019. Application of machine and deep learning strategies for the classification of heritage point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W18.10.5194/isprs-archives-XLII-4-W18-447-2019
  6. Gross, H., Jutzi, B., Thoennessen, U., 2007. Segmentation of tree regions using data of a fullwavform laser. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVI-3/W49A:57–62.
  7. Hughes, G. F., 1968. On the Mean Accuracy of Statistical Pattern Recognizers. IEEE Transactions on Information Theory, IT-14, pp. 55-63.10.1109/TIT.1968.1054102
  8. King, D. E., 2009. Dlib-ml: A Machine Learning Toolkit. Journal of Machine Learning Research, 10, pp. 1755-1758.
  9. Lee, I., Schenk, T., 2002. Perceptual organization of 3D surface points. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXIV (3A), pp. 193–198.
  10. Li, N., Liu, C., Pfeifer, N., 2019. Improving LiDAR classification accuracy by contextual label smoothing in postprocessing. ISPRS Journal of Photogrammetry and Remote Sensing, 148, pp. 13-31.10.1016/j.isprsjprs.2018.11.022
  11. Lin, C.-H., Chen, J.-Y., Su, P.-L., Chen, C.-H., 2014. Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 94, pp. 70-79.10.1016/j.isprsjprs.2014.04.016
  12. Niemeyer, J., Rottensteiner, F., Soergel, U., 2012. Conditional random fields for LiDAR point cloud classification in complex urban areas. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, pp. 263–268.10.5194/isprsannals-I-3-263-2012
  13. Rabbani, T., Heuvel, F.A., Vosselman, G., 2006. Segmentation of point clouds using smoothness constraint. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36, pp. 248-253.
  14. Shannon, C. E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27(3), pp. 379–423.10.1002/j.1538-7305.1948.tb01338.x
  15. Serna, A., Marcotegui, B., 2014. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 10.1016/j.isprsjprs.2014.03.015.10.1016/j.isprsjprs.2014.03.015
  16. Weinmann, M., Jutzi, B., Mallet, C., 2014. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3, pp. 181-188.10.5194/isprsannals-II-3-181-2014
  17. Weinmann, M., Schmidt, A., Mallet, C., Hinz, S., Rottensteiner, F. et al., 2015. Contextual classification of point cloud data by exploiting individual 3D neigbourhoods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3, pp. 271-278.10.5194/isprsannals-II-3-W4-271-2015
  18. Wen, C., Yang, L., Li, X., Peng, L., Chi, T., 2020.Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification, ISPRS Journal of Photogrammetry and Remote Sensing, 162, pp. 50-62.10.1016/j.isprsjprs.2020.02.004
  19. Zhao, K., Popescu, S., Nelson, R., 2009. Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment, 113, pp. 182–196.10.1016/j.rse.2008.09.009
  20. Jolliffe, I.T., 2002: Principal Component Analysis. Springer Series in Statistics, New York, pp. 10-28.
  21. Weinmann, M., 2016: Reconstruction and Analysis of 3D Scenes. Springer, Karlsruhe, pp. 39-51, 152-164.10.1007/978-3-319-29246-5
  22. Anguelov, D., Taskarf, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A., 2005. Discriminative learning of Markov Random Fields for segmentation of 3D scan data. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, pp. 169–176.
  23. Gaurav, S. G., 2017. Light Detection and Ranging (LiDAR): Technologies and Global Markets. BCC Research report.
  24. Hall, M. A., 1999. Doctoral Thesis. Correlation-based Feature Selection for Machine Learning, University of Waikato, Hamilton, New Zealand, pp. 25-51.
  25. Weinmann, M., 2013. Visual Features—From Early Concepts to Modern Computer Vision. In: Advanced Topics in Computer Vision, Springer, London, pp. 1-34.10.1007/978-1-4471-5520-1_1
  26. Chen., I-L., Kuo, B.-C., Li, C.-H., Hung, C.-C., Combining ensemble technique of support vector machines with the optimal kernel method for high dimensional data classification. Powerpoint Presentation, July 28, 2011.
  27. DLIB, 2019: https://dlib.net (view at 21 May 2020).
  28. Eigenvectors, 2020: http://mriquestions.com/dti-tensor-imaging.html (view at 5 September 2020).
  29. PCL, 2019: https://pcl.readthedocs.io/projects/tutorials/en/latest/region_growing_segmentation.html?highlight=region (view at 07 June 2020).
  30. VOLTA, 2017: http://volta.fbk.eu (view at 29 September 2020).
Language: English
Page range: 155 - 162
Submitted on: May 30, 2020
|
Accepted on: Jul 10, 2020
|
Published on: Nov 11, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2020 Iuliana Maria Pârvu, E. Özdemir, F. Remondino, published by University of Oradea, Civil Engineering and Architecture Faculty
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.