The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Figure 1. The study area of the City of Alkhabra, Saudi Arabia 
3. DATA ANALYSIS AND RESULTS 
To produce satellite image map, good quality satellite data have 
been selected. Processing procedure includes geometric 
correction, contrast stretching, mosaicking, filtering and area 
cutting. Band combination is made available both 321 in true 
color. All image-processing analysis was carried out using PCI 
Geomatica version 9.1.8 software at the School of Physics, 
University of Science Malaysia (USM). Figure 2 shown a 
satellite image map at scales of 1:91,831.00. 
LEGEND 
m Urban Area 
H| Road 
Vegetation 
|4 Sand/Land 
Figure 2. The satellite image map of the City of Alkhabra, 
Saudi Arabia 
The images analysis involved three basic steps in supervised 
classification: the training stage, the classification stage and the 
output stage. Training sites were needed for supervised 
classification. In the training stage, the areas were established 
using polygons. They are delineated by spectrally homogeneous 
sub areas, which have, class name given. In the classification 
stage, three supervised classification methods were selected to 
classify the video images. Maximum Likelihood, Minimum 
Distance-to-Mean, and Parallelepiped were applied in the 
present study. 
In the output stage, the classification map is a thematic map of 
the land cover over Penang Island, Penang. Many methods of 
accuracy assessment have been discussed in remote sensing 
literatures. Kappa statistic was used in this study. It is widely 
used because all elements in the classification error matrix, and 
not just the main diagonal, contribute to its calculation and 
because it compensates for change agreement (Selamat, et al., 
2002). Kappa coefficients were generated to describe the 
proportion of agreement between the classification result and 
the validation sites after random agreements by chance are 
removed from consideration these data (Thomas, et. al., 2002). 
Three measures of accuracy were tested in this study, namely 
overall accuracy, error matrix and Kappa coefficient. In 
thematic mapping from remotely sensed data, the term accuracy 
is used typically to express the degree of ‘correctness’ of a map 
or classification (Foody, 2002). Figure 3 shows the flow chart 
for data processing of the images. The produced results in this 
study are shown in Table 1 and the accuracy assessment results 
were shown in Table 2. In this study, Maximum Likelihood 
classifier was found to produce the best accuracy. Finally, the 
land cover/use map of the desert area was classified using 
Maximum Likelihood classifier and shown in Figure 4. 
Figure 3. Flow chart for data processing of the image 
Classification method 
Kappa coefficient 
Maximum Likelihood 
0.9258 
Minimum Distance-to- 
Mean 
0.8369 
Parallelepiped 
0.63285 
Table 1. The Kappa coefficient for the image