International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
  
data were treated as independent spectral classes within 
each informational class. This means that the selected 
patches were not aggregated into composite statistics 
for the seven classes. The likelihood index for each 
patch was computed for all individual training data set 
patches by the divergence index, the maximum 
likelihood using the patch mean, and the patch pdf. 
Small patches with fewer than six pixels were excluded 
from the region-based maximum likelihood analysis, 
and treated as part of the “melt pond,” to use McDevitt 
and Peddada's (1998) term. There were two reasons 
for identifying melting pond pixels. Firstly, because 
five variables were used in this study, patches with 
fewer than six pixels had less than the minimum 
number of pixels potentially required to characterize 
the multivariate statistics. Secondly, the melting pond 
was assumed to represent objects that are not of direct 
interest, but rather extraneous objects such as cars, or 
chimneys on buildings. 
In the third stage, each patch was classified into seven 
classes by the suggested methods. For maximum 
likelihood with patch pdf, the range over which the pdf 
was calculated was limited to three standard deviations. 
The pdf is very low outside of this range, and is not 
expected to have much significance in the calculation. 
Excluding pdf values greater than three standard 
deviations has the advantage of reducing the computing 
cost. 
Figure 4 shows a one dimensional representation of the 
process. Within the pdf overlap region, the decision 
range was divided into ten equal cells. The 
probability of the center of each cell calculated for both 
the training and the patch classes, and the lower of the 
two probabilities is used for the cell height. After 
multiplying cell height by the width, the cell area is 
calculated. The total area of the overlap is then 
estimated by summing the cell areas (Figure 4). This 
procedure is modified for the multivariate case by 
dividing the multidimensional overlap region into 10" 
cells, where n is the number of bands. For two bands 
a volume of the overlap region is calculated, and for 
three or more bands a hypervolume is calculated. For 
this work, five bands were used, thus, 10° cells were 
calculated for each likelihood index. 
Training patch Tested patch 
  
  
Decision making range % 
Figure 4. Maximum likelihood calculation utilizing 
patch pdfs. 
The patch was assigned to the class with the highest 
likelihood after the unknown patch is compared with 
each patch in the training data set. In the next step of 
the classification, melting pond pixels are classified. 
These small patches are treated as noise, and therefore 
assigned to an adjacent class. If the patch is 
surrounded by a single class, it is assigned to that class. 
In the general case, however, the patch is adjacent to 
more than one class. In this case, the patch is assigned 
to the adjacent class with the most similar DN values in 
the green band (Band 2). A more sophisticated, 
multivariate approach was not used because of the 
small sample size of these patches. In the final step, 
adjacent patches of the same class were merged to form 
objects. 
ERDAS Imagine was used to conduct the traditional 
pixel-based classifications. The unsupervised 
ISODATA program (Tou and Gonzalez, 1974; 
ERDAS, 1999) was executed with 24 clusters. After 
classification, the 24 clusters were assigned empirically 
to the most appropriate class among the seven classes 
based on the ground truth and knowledge of the area. 
For each of the supervised classification methods, the 
same training data sets were used. 
S. RESULTS AND DISCUSSION 
Figure 5 shows the results from the four previously 
mentioned methods. To compare the accuracy of the 
four methods, error matrices for the kappa index and 
errors of commission and omission were produced 
using the IDIRSI program ERRMAT (Eastman, 2003) 
(Table 1). Ground reference maps for the accuracy 
evaluation were produced using photo-interpretation 
and expert knowledge for three parts of the study area: 
Downtown Morgantown, a medium density residential 
area, and a forested stream valley. 
Table 1. Summary accuracy statistics for 7 classes by 
the 4 classification methods used in this study. 
  
   
     
   
    
    
    
    
    
    
   
   
   
   
   
    
   
    
  
  
  
   
   
   
   
   
   
    
    
   
    
   
    
  
  
  
  
  
  
Blding Road Forest Lawn 
CERR 0.363 0.489 0.135 0.440 
ISODATA 
OERR 0.487 0.320 0.147 0.046 
MHL with | CERR | 0.391 0347 | 0063 | 0366 
pixel OERR | 0.202 0.412 0.133 0.214 
MLH with CERR 0.309 0.291 0.104 0.304 
  
patch mean | OERR | 0.194 0.292 0.042 0.503 
  
Un 
MLE with | CERR | om 0.2043 | 0.101 0.294 
  
  
  
  
  
  
  
  
  
  
  
  
patchpdf | OERR | 0.170 0.249 0.045 0.480 
Shd Veg Water Shadw Kappa 
CERR 0.567 0.024 0.062 
ISODATA - 0.610 
OERR 0.373 0.981 0.352 
MHL with CERR 0.440 0.123 0.137 
1 0.687 
pixel OERR 0.297 0.308 0.359 
MLH with | CERR 0.360 0.095 0.048 
patch = s s ; 0.735 
eat OERR 0.691 0.232 0.398 
MLH with; |-.CÉRR 0.306 0.042 0.054 m 
patch pdf | OERR 0.603 0.089 0.234 C 
  
  
  
  
  
  
  
  
The overall kappa value of the supervised pixel-based 
classifications was 0.687. The lowest accuracy, 0.610, 
was obtained with the unsupervised pixel-based