Lidar information extraction by attribute filters with partial reconstruction

Wenzhi Liao, Mauro Dalla Mura, Xin Huang, Jocelyn Chanussot, Sidharta Gautama, Paul Scheunders, Wilfried Philips, Ji WU, Yaqiu JIN, Jiancheng SHI

Research output: Contribution to conferencePaper

3 Citations (Scopus)

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.

Conference

Conference2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Abbreviated titleIGARSS 2016
CountryChina
CityBeijing
Period10/07/1615/07/16

Fingerprint

Optical radar
optical radar
lidar
filter
filters
forest management
urban planning
parking
Urban planning
disasters
multisensor fusion
Parking
Forestry
Data fusion
roads
Disasters
attribute
detection
remote sensing
Remote sensing

Keywords

  • information extraction
  • morphological attribute filters
  • remote sensing
  • LiDAR
  • hyperspectral sensors
  • optical radar
  • image reconstruction

Cite this

Liao, W., Dalla Mura, M., Huang, X., Chanussot, J., Gautama, S., Scheunders, P., ... SHI, J. (2016). Lidar information extraction by attribute filters with partial reconstruction. 1484-1487. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China. https://doi.org/10.1109/IGARSS.2016.7729379
Liao, Wenzhi ; Dalla Mura, Mauro ; Huang, Xin ; Chanussot, Jocelyn ; Gautama, Sidharta ; Scheunders, Paul ; Philips, Wilfried ; WU, Ji ; JIN, Yaqiu ; SHI, Jiancheng. / Lidar information extraction by attribute filters with partial reconstruction. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.4 p.
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title = "Lidar information extraction by attribute filters with partial reconstruction",
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, hyperspectral sensors, optical radar, image reconstruction",
author = "Wenzhi Liao and {Dalla Mura}, Mauro and Xin Huang and Jocelyn Chanussot and Sidharta Gautama and Paul Scheunders and Wilfried Philips and Ji WU and Yaqiu JIN and Jiancheng SHI",
year = "2016",
month = "11",
day = "3",
doi = "10.1109/IGARSS.2016.7729379",
language = "English",
pages = "1484--1487",
note = "2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2016 ; Conference date: 10-07-2016 Through 15-07-2016",

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Liao, W, Dalla Mura, M, Huang, X, Chanussot, J, Gautama, S, Scheunders, P, Philips, W, WU, J, JIN, Y & SHI, J 2016, 'Lidar information extraction by attribute filters with partial reconstruction' Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10/07/16 - 15/07/16, pp. 1484-1487. https://doi.org/10.1109/IGARSS.2016.7729379

Lidar information extraction by attribute filters with partial reconstruction. / Liao, Wenzhi; Dalla Mura, Mauro; Huang, Xin; Chanussot, Jocelyn; Gautama, Sidharta; Scheunders, Paul; Philips, Wilfried; WU, Ji; JIN, Yaqiu; SHI, Jiancheng.

2016. 1484-1487 Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Lidar information extraction by attribute filters with partial reconstruction

AU - Liao, Wenzhi

AU - Dalla Mura, Mauro

AU - Huang, Xin

AU - Chanussot, Jocelyn

AU - Gautama, Sidharta

AU - Scheunders, Paul

AU - Philips, Wilfried

AU - WU, Ji

AU - JIN, Yaqiu

AU - SHI, Jiancheng

PY - 2016/11/3

Y1 - 2016/11/3

N2 - 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.

AB - 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.

KW - information extraction

KW - morphological attribute filters

KW - remote sensing

KW - LiDAR

KW - hyperspectral sensors

KW - optical radar

KW - image reconstruction

UR - http://hdl.handle.net/1854/LU-7181958

U2 - 10.1109/IGARSS.2016.7729379

DO - 10.1109/IGARSS.2016.7729379

M3 - Paper

SP - 1484

EP - 1487

ER -

Liao W, Dalla Mura M, Huang X, Chanussot J, Gautama S, Scheunders P et al. Lidar information extraction by attribute filters with partial reconstruction. 2016. Paper presented at 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China. https://doi.org/10.1109/IGARSS.2016.7729379