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Technical Commission VII

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia

Ahmed Shaker ^ *, Nagwa El-Ashmawy ^^
? Ryerson University, Civil Engineering Department, Toronto, Canada - (ahmed.shaker, nagwa.elashmawy)@ryerson.ca
? Survey Research Institute, National Water Research Center, Cairo, Egypt
Commission VII, WG VII/4
KEY WORDS: LiDAR, intensity data, Land Cover classification, PCA.
Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined
by the LiDAR sensors. LIDAR sensors record the distance between the sensor and the targets (range data) with a capability to record
the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum
range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the
LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LIDAR data only
(range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights,
Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on
the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LIDAR Data as
one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia,
Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas,
trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the
intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis
(PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the
four bands extracted from the Principal Component Analysis (PCA).
Light Detection and Ranging (LiDAR) is a remote sensing
technique used mainly for 3D data acquisition of the Earth
surface and its applications in the 3D City modelling and
building extraction and recognition, (Haala & Brenner, 1999,
Song et al, 2002, Brennan and Webster, 2006, Hui et al., 2008,
and Yan & Shaker, 2010). LiDAR sensors transmit laser pulses
in near infrared (NIR) spectrum range toward objects and record
the reflected energy. The distances between the LiDAR sensor
and the targets (range data) are calculated. The 3D coordinates
of the collected points are calculated from the range data with
the aid of other sensors (GPS, and IMU), (Ackerman, 1999).
LiDAR is considered as highly precise and accurate vertical and
horizontal data acquisition system (Brennan and Webster,
2006). The high accurate data are used for generating digital
elevation and/or surface models (DTM/DSM), Kraus & Pfeifer,
(1998) used LiDAR data to create DTM in wooded areas. The
accuracy of the DTM extracted was 25 cm for flat areas, which
is improved to 10 cm by refining the data processing method.
In the last decade, substantial work is done to combine the
LiDAR data with other external data such as aerial photos and
satellite images for information extraction. Haala & Brenner
(1999) combined LiDAR elevation data and a multi-spectral
aerial photo (Green, Red and NIR bands) for building extraction
using unsupervised classification technique. It was found that

* Corresponding author:
combining the multi-spectral aerial photo with the LiDAR
elevation data improved the classification results significantly.
LiDAR sensors not only record the time difference between
sending and receiving signals; but they also record the
backscattered energy from the targets (intensity data) in NIR
spectrum range. A NIR image can be generated by interpolating
the intensity data collected by the LiDAR sensors. With the
capability to record the intensity of the reflected energy,
definition of the classification of LiDAR data is not only
referring to the separation of terrain and non-terrain features,
but it includes the use of the intensity data for the classification
of land covers as well. Hence, intensity data is investigated to
be used to distinguish different target materials using various
image classification techniques.
Recently, the use of the LiDAR intensity and range data has
been studied for data classification and feature extraction. The
intensity data were used primarily as a complementary data for
data visualization and interpretation. LIDAR intensity data are
advantageous over the multi-spectral remote sensing data in
avoiding the shadows appear in the multi-spectral data. This is
because LiDAR sensor is an active sensor. Hui et al., 2008,
used the intensity and height LiDAR data for land-cover
classification. Supervised classification technique was used to
differentiate four classes: Tree, Building, Bare Earth and Low
Vegetation. It was observed that combining the intensity data
with the height data is an effective method for LiDAR data
classification. However, quantitative accuracy assessment was
not included in that research work.
Ahmed Shaker, Ryerson University, Department of Civil Engineering,
350 Victoria St., Toronto, Canada, M5B 2K3
E-mail: ahmed shaker@ryerson.ca
Tel: +1 416 979 5000 (ext. 4658)