Recent researchers combine the laser data with other auxiliary 
data such as multispectral aerial photos or satellite images, 
USGS DEM, texture data, normalized height, and multiple- 
returns data. Charaniya et al, (2004) used LiDAR height and 
intensity data, height variation data, multiple-return data, USGS 
DEM, and luminance data of a panchromatic aerial imagery for 
land-cover classification. A supervised classifier was used to 
distinguish four classes: trees, grass, roads, and roofs. The effect 
of band combinations on the classification results was studied. 
It was observed that height variation affected positively the 
classification results of the high vegetation areas, Luminance 
and intensity data was useful for distinguishing the roads from 
the low vegetation areas, and the multiple-return differences 
slightly improved the classification of roads and buildings but 
reduced the accuracy of the other classes. 
Subsequently, researchers gave more attention to the intensity 
data and started to analyse the data and study different 
enhancing methods to remove the noise and improve the data 
interpretation. Song et al, (2002) examined different resampling 
techniques to convert LiDAR point data to grid image data 
which is filtered to remove the noise with minimum influence 
on the original data. The resampled grid is used to investigate 
the applicability of using the LiDAR intensity data for land- 
cover classification. It is concluded that the LiDAR intensity 
data contain noise that is needed to be removed. 
Radiometric correction of the intensity data was suggested in 
some of the recent literatures (Coren and Sterzia, 2006; Hófle 
and Pfeifer, 2007). The process mainly relies on the use of the 
laser range equation to convert the intensity data into the 
spectral reflectance with consideration of the scanning 
geometry, the atmospheric attenuation, and the background 
backscattering effects. After the radiometric correction, the 
homogeneity of the land cover is improved and thus enhances 
the performance of feature extraction and surface classification. 
Yan et al. (2012) evaluated the accuracy of different land cover 
classification scenarios by using the airborne LiDAR intensity 
data before and after radiometric correction. An accuracy 
improvement of 8% to 12% was found after applying the 
radiometric correction. 
This research investigates the use of the intensity data for land- 
cover information extraction. The Maximum Likelihood 
supervised classification technique is proposed and applied on 
two different study areas, and classification accuracy is assessed 
to recommend the most appropriate data combinations for such 
areas. The paper is divided into five sections. Section 1 is the 
introduction which highlights the previous work related to the 
use of LIDAR data in land cover information extraction. Section 
2 comprises the methodology used in this research work. 
Section 3 describes the study areas and the datasets used. 
Section 4 includes the results of the experimental work and the 
analysis. The paper is concluded by a summary of the work and 
the future work in Section 5. 
2. METHODOLOGY 
The work is conducted in two main steps; data preparation, and 
data classification and assessment. In the data preparation step, 
the point data recorded by the LiDAR sensor are converted into 
raster image data, prepared as bands, to be used for the 
classification step. The bands prepared are also combined and 
Principal Component Analysis (PCA) is used to produce 
principal component bands for more investigation. 
The second step is applying the classification algorithm on the 
different prepared datasets, and assessing the results. Four 
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 
   
information classes are identified in this study area. Details of 
the work procedure are discussed in the following sub-sections. 
2.1 Data Preparation 
LiDAR Point 
Clouds 
(x, y, z, I) 
  
  
  
  
  
  
Data 
Separation 
Intensity points DSM points Terrain points 
(x y, 1) (x y, z) (x, y, z) 
  
  
  
  
  
  
  
| | 
Conversion to Raster 
Data 
Y Y Y 
Radiometric Digital Surface Digital Terrain 
Corrected Itenstty Model Image Model Image 
(RCI) Ds (DTM) 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Height 
Calculation 
  
Normal Heights 
(NH) Surface 
  
  
c 
  
  
  
  
   
Spatial 
Enhancement 
y 
Topographic 
Analysis 
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
   
  
  
  
Texture of Slope of DSM Slope of NH 
Intensity Data Data Data 
1 2 3 
Band 
Combination 
i i i i y l 
RCland DSM| |RCI, DSM and RCI, DSM, RCI and NH RCI, NH and RCI, NH, 
d Texture Texture and g Texture Texture and 
© Slope h Siope 
  
  
  
  
  
  
  
  
  
  
  
  
  
     
    
    
    
     
Principal 
Component 
Analysis 
(PCA) 
Principal 
Component 
Analysis 
(PCA) 
  
Principal Principal 
Component Component 
Bands Bands 
i k 
  
  
  
  
Figure 1: Work Flow of Data Preparation 
The data sets are prepared by converting the data collected by 
the sensor (range and intensity data) into raster image data. 
Since the multi-returned data are not available, the terrain has 
been separated manually from object surface by selecting the 
point data that falls on the roads and the terrain. The Kriging 
interpolation algorithm is used for point data conversion into 
image data. New image data (bands) are created representing 
the followings: i) DSM, ii) DTM, iii) Intensity, iv) Normal