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, NH, 
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Heights (NH, calculated from the difference between the DSM 
and the DTM data). Other auxiliary image data (bands) are 
created to be used in the classification process. The auxiliary 
bands are: the texture from the intensity data, and the slope 
from both the DSM and the NH. 
Several images are produced by combining two or more of the 
created bands. The image band combinations can be 
summarized as follows: bands of a) intensity and elevation, b) 
intensity, elevation and texture, c) intensity, elevation, texture, 
and slop. 
The principal components are also created from the existing 
four bands to reduce the number of existing bands by 
eliminating the correlated ones. The classification analysis 
includes both datasets; the one created directly from the original 
LiDAR data (range and intensity), and the auxiliary datasets 
extracted from the original data. Figure 1 illustrates the work 
flow of the data preparation step. 
2.2 Data Classification and Evaluation 
The second part of the study work covers the classification 
process and the classification assessment of the results. The 
Maximum Likelihood classifier, as a supervised classification 
algorithm, is used with the bands created directly from the 
LiDAR data, and with the six band combinations mentioned 
above. The classification and evaluation is repeated for bands 
created from the PCA. The classification processes for all 
datasets (different band combinations) are summarized as 
follows: 
1) Training signatures are identified for four different classes 
(trees, grass, buildings, and roads). 
2) Statistical assessments of the training signatures are done and 
further enhancement to the selection of the training areas are 
taken place, if required. 
3) Maximum Likelihood algorithm is applied and the image 
data is classified into the corresponding classes. 
4) Assessment the results of the classification using ground 
truth data and by performing evaluation using error matrix. The 
classification process is evaluated using about 1000 reference 
points, for each study area, that are randomly selected from the 
original point cloud data to avoid the effect of the interpolation 
on the accuracy of the ground truth. The well-distributed points 
over the study area are randomly generated. The ground truth 
information is collected from the ortho-rectefied aerial photo 
provided with the LiDAR data. Finally, the accuracies achieved 
from classification results of the different band combinations 
are compared. 
3. STUDY AREA AND DATA SETS 
3.1 Study Area 
A study area is chosen, which covers a part of the British 
Columbia Institute of Technology (BCIT) located in the 
Burnaby, British Columbia, Canada (122?59' W, 49?15"N). An 
area of 500 m x 400 m is selected for the experimental work 
because it contains a variety of the land cover features on the 
ground including; buildings, parking areas, trees and open 
spaces with grassy coverage, (Figure 2 ). 
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 
  
  
  
  
Figure 2: Study Area (British Colombia Institute of Technology, 
Vancouver) 
3.2 Data Sets 
A Leica ALS50 sensor operating in 1.064 um wavelength and 
0.33 mrad beam divergence is used for the LiDAR acquisition 
mission on July 17, 2009 at local time 14:55. The flying height 
for this mission was around 600m. The data acquired contains a 
3D point cloud (x, y, and z coordinates) and linearized intensity 
values (I) for each point. 
c d 
Figure 3: a) Geometrically Calibrated and Radiometrically 
corrected Intensity Image, b) DSM, c) NH, and d) Ortho-rectified 
Aerial Photo 
The provided dataset for this experimental work is: an Ortho- 
rectified aerial photo for the study area, and geometrically 
calibrated and radiometrically corrected LiDAR data (including 
x, y, z and I), the aerial photo is acquired at the same time of the 
LiDAR data acquisition mission. The related geometrical 
calibration and radiometrical correction works are illustrated in 
Yan et. al. (2012). The LiDAR data (intensity and rang) are 
converted to raster image with pixel size equals to 20 cm to 
produce the intensity image and the DSM. The LIDAR points 
fell on the ground are separated, and a DTM is produced using 
these points with the same pixel size of 20 cm. The difference