ul 2004 
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©. Oo 
  
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
classifier ISODATA. The maximum likelihood 
classifier using the patch mean resulted in a relatively 
high kappa value of 0.735. Maximum likelihood 
classifier with pdf produced the overall best accuracy, 
0.783. 
Looking at the results in more detail, the unsupervised 
classifier resulted in many isolated pixels and small 
clusters, as expected (Figure 5 a). The Water class in 
the region of the stream was almost completely 
misclassified as Building with this method. The 
stream has exposed and shallow covered rock that is 
apparently spectrally similar to the materials from 
which buildings are constructed. Building was also 
misclassified as Road, and consequently the Building 
omission error was relatively high (Table 1). Pixel- 
based supervised classification (Figure 5 b), like the 
unsupervised classification, resulted in a rather noisy 
classification. The classes of Buildings and Roads 
were extensively confused, resulting in high errors of 
commission and omission for both classes. However, 
compared to the unsupervised classification, the 
confusion between Building and Water was 
dramatically reduced for the pixel-based maximum 
likelihood classification. 
The maximum likelihood classifier using the patch 
mean (Figure 5 c) yielded a visually pleasing 
classification, and the second best overall accuracy. 
The higher classification accuracy of the maximum 
likelihood classification with patch pdf is most likely a 
result of the incorporation of differences in the kurtosis 
of classes through the variance-covariance matrix data. 
When only the patch mean is used in the classification, 
such differences are suppressed. The particular 
classes that were less well classified in the maximum 
likelihood using the patch mean, compared to the patch 
pdf, were the Building and Road classes. But the 
computing cost for classification with the mean was 
much lower than with the pdf. Thus, the classifier 
with the patch mean is an efficient alternative to 
classification with pdf. 
The maximum likelihood classification with pdf 
produced higher accuracy than any other classifier 
(Table 1). The segmentation suppresses isolated 
pixels and small clusters (Figure 5 d), and thus 
classification error resulting from high within object 
variance was efficiently controlled by this method. 
However, a number of cases of confusion arose 
between Building and Road, and Lawn and Forest. 
The confusion between Lawn and Forest can be related 
to segmentation. Although these two classes 
generally had sufficient spectral difference between 
them for good classification, in some cases the low 
  
  
Legend 
Building Forest ME Shadowed vegetation 
Road Lawns ME Water ME Other shadow 
Figure 5. Results of the classifications. (Above) 
Legend. (Right) a): ISODATA from ERDAS 
Imagine. b): Maximum likelihood classification from 
ERDAS IMAGINE. c): Maximum likelihood classifier 
with patch mean. d): Maximum likelihood classifier 
with patch pdf.