—- d
the land cover classification accuracy. The results of land cover
classification of the PCA cases are shown in Figures 5, cases j
and k.
The overall accuracy calculated based on the 1000 ground truth
points for all the cases is listed in Table 1. The results obtained
show that the overall accuracy by using the intensity and the
DSM data individually are less than 45%, (Table 1, case a, and
b). Combining both the intensity and the DSM data improves
the results to 55% (Table 1, case d). Using the normal height
band individually does not improve the accuracy. This is
because of the similarity between the heights of the trees and
the buildings, as well as due to the similarity in heights between
the roads and the grass. Nevertheless, combining the normal
heights data with the intensity data has a significant
improvement in the overall accuracy of the classification results.
An overall accuracy of about 70% can be achieved as it is seen
in Table 1 (case e). It is also observed that the overall accuracy
of the classification results is increased by combining the
texture of the intensity data to the intensity and elevation data,
(cases f and g using intensity texture comparing to cases dande
without using texture, respectively). Yet, combining the slope of
the elevation data with the intensity, the elevation, and the
texture data does not improve the overall classification
accuracy. For the principle component analysis the accuracy of
results comparable to the classification results combined
images. Further work are planned to investigate more bands
created from the LiDAR data.
Table 1: Accuracy assessments of the land cover classification
Case | Band Combination Areal
a Intensity 43.7%
b DSM 43.1%
e NH 52.5%
d Intensity, DSM 55.1%
e Intensity, NH 72.2%
f Intensity DSM, Texture 57.9%
g Intensity NH, Texture 77.2%
h Intensity, DSM, Texture, Slope 59.8%
i Intensity NH, Texture, Slope 73.3%
j PCA of (Intensity, DSM, Texture, DSM Slope) 62.6%
k PCA of (Intensity, NH, Texture, NH Slope) 70.9%
5. CONCLUSIONS
This research work examines the use of the LiDAR data only
(range and intensity data) for Land-Cover information
extraction. Different image bands (Intensity, DSM, Normal
Height, Intensity Texture, DSM Slope, and Normal Height
Slope) are created from the LiDAR points recorded by Leica
ALS50 sensor. In addition, components of the principle
component analysis are generated to be used for the land cover
classification process. LiDAR dataset covering an area of the
British Columbia Institute of Technology (BCIT) is classified
using the Maximum likelihood classifier, and around 1000
ground truth points were used for the accuracy assessment.
From the results obtained, it is observed that using the LiDAR
original data (range and intensity) individually in the
classification process introduce an overall accuracy of less than
45%. However, using both the range and the intensity data
improves the results accuracy by approximately 10%. Adding
auxiliary data, such as Texture of the intensity data and surfaces
slope, slightly improves the accuracy of the land cover
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
classification. Using the normal heights as elevation data
instead of the DSM, improves the accuracy of the classification
results significantly, (from 55% to more than 72%).
Components of the Principle Component Analysis (PCA)
created from the LiDAR original and auxiliary data can also be
used. Similar overall accuracy to the results achieved by using
the original and the auxiliary data can be achieved (about 70%).
Further research work is underway to further investigate the
PCA using more bands extracted from the LiDAR and other
sensor data to improve the classification accuracy.
ACKNOWLEDGMENT
This research work is supported by the Discovery Grant from
the Natural Sciences and Engineering Research Council of
Canada (NSERC) and the GEOIDE Canadian Network of
Excellence, Strategic Investment Initiative (SIT) project SII P-
IV # 72. The authors would like to thank McElhanney
Consulting Services Ltd, BC, Canada for providing the real
LiDAR and image datasets to the GEOIDE project.
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