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  • This work was supported by FWO (Fund for Scientific Research in Flan- ders) project G037115N “Data fusion for image analysis in remote sensing.”
Abstract
Recent advances in airborne light detection and ranging (LiDAR) technology allow us to rapid measure the topographical information over large areas. LiDAR remote sensed data has been widely used in many applications, e.g. forest management, urban planning, disaster predictions, etc. However, extracting useful information from LiDAR data remains challenging, especially in the urban remote sensing, where many objects have the same elevation and are connected, such as road and parking lots, trees and buildings. In this work, we present a new method to extract geometric and textural information from LiDAR data by using attribute filters with partial reconstruction. The proposed method can separate the connected objects and better model the geometric and textural information than traditional connected filters (e.g. attribute filters). Experimental results on LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using original LiDAR data or attribute profiles computed by traditional attribute filters, with the proposed method, overall classification accuracies were improved by 35% and 12%, respectively.
Keywords
information extraction, morphological attribute filters, Remote sensing, LiDAR

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MLA
Liao, Wenzhi, et al. “Lidar Information Extraction by Attribute Filters with Partial Reconstruction.” 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), edited by Ji WU et al., IEEE, 2016, pp. 1484–87.
APA
Liao, W., Dalla Mura, M., Huang, X., Chanussot, J., Gautama, S., Scheunders, P., & Philips, W. (2016). Lidar information extraction by attribute filters with partial reconstruction. In J. WU, Y. JIN, & J. SHI (Eds.), 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) (pp. 1484–1487). Beijing: IEEE.
Chicago author-date
Liao, Wenzhi, Mauro Dalla Mura, Xin Huang, Jocelyn Chanussot, Sidharta Gautama, Paul Scheunders, and Wilfried Philips. 2016. “Lidar Information Extraction by Attribute Filters with Partial Reconstruction.” In 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), edited by Ji WU, Yaqiu JIN, and Jiancheng SHI, 1484–87. IEEE.
Chicago author-date (all authors)
Liao, Wenzhi, Mauro Dalla Mura, Xin Huang, Jocelyn Chanussot, Sidharta Gautama, Paul Scheunders, and Wilfried Philips. 2016. “Lidar Information Extraction by Attribute Filters with Partial Reconstruction.” In 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), ed by. Ji WU, Yaqiu JIN, and Jiancheng SHI, 1484–1487. IEEE.
Vancouver
1.
Liao W, Dalla Mura M, Huang X, Chanussot J, Gautama S, Scheunders P, et al. Lidar information extraction by attribute filters with partial reconstruction. In: WU J, JIN Y, SHI J, editors. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). IEEE; 2016. p. 1484–7.
IEEE
[1]
W. Liao et al., “Lidar information extraction by attribute filters with partial reconstruction,” in 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), Beijing, 2016, pp. 1484–1487.
@inproceedings{7181958,
  abstract     = {Recent advances in airborne light detection and ranging (LiDAR) technology allow us to rapid measure the topographical information over large areas. LiDAR remote sensed data has been widely used in many applications, e.g. forest management, urban planning, disaster predictions, etc. However, extracting useful information from LiDAR data remains challenging, especially in the urban remote sensing, where many objects have the same elevation and are connected, such as road and parking lots, trees and buildings. In this work, we present a new method to extract geometric and textural information from LiDAR data by using attribute filters with partial reconstruction. The proposed method can separate the connected objects and better model the geometric and textural information than traditional connected filters (e.g. attribute filters). Experimental results on LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using original LiDAR data or attribute profiles computed by traditional attribute filters, with the proposed method, overall classification accuracies were improved by 35% and 12%, respectively.},
  author       = {Liao, Wenzhi and Dalla Mura, Mauro and Huang, Xin and Chanussot, Jocelyn and Gautama, Sidharta and Scheunders, Paul and Philips, Wilfried},
  booktitle    = {2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)},
  editor       = {WU, Ji and JIN, Yaqiu and SHI, Jiancheng},
  isbn         = {978-1-5090-3332-4},
  issn         = {2153-6996},
  keywords     = {information extraction,morphological attribute filters,Remote sensing,LiDAR},
  language     = {eng},
  location     = {Beijing},
  pages        = {1484--1487},
  publisher    = {IEEE},
  title        = {Lidar information extraction by attribute filters with partial reconstruction},
  url          = {http://dx.doi.org/10.1109/IGARSS.2016.7729379},
  year         = {2016},
}

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