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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
It is clear from Table 5 that classification accuracies produced 
for Landsat ETM+ and Terra ASTER images (C1 and C2) are 
higher than those produced by these images together with their 
principal components (C3 and C4). This shows the 
ineffectiveness of PCA bands for land cover type delineation. 
While the PCA bands do not introduce different distinctive 
characteristics, they increase the dimensions and the 
complexity of the data. Therefore, they do not make any 
significant contribution to the results produced by the two 
classifiers. On the other hand, the use of the combination of the 
two multi-temporal images (C5) in classification resulted in 
slightly better performances. However, it should be borne in 
mind that it is difficult to increase the level of classification 
accuracy after a certain point, mainly depending on the 
complexity of the data set, the number of output classes to be 
recognised and the classification technique used. 
After the accuracy assessment stage, subset images were fed 
into the trained networks to produce the thematic maps of the 
study area including eight land cover classes. As an example, 
the ML and ANN classification results for C6 combination are 
given in Figure 3. The robustness of ANN classification over 
ML classification can be easily observed from the figure, 
especially for road class. In the ML classification many pixels 
were identified as road since the samples collected for this 
particular class are known to be mostly mixed pixels that 
encompass a substantial region in the feature space compared 
to the others. This forced the ML, which relies on statistical 
estimates, to wrongly identify many pixels as road in the 
resulting thematic image. The weakness of the ML 
classification can be also observed for urban pixels near the 
upper right corner of the image. Except for these problems, the 
ML classification method performed well for other classes. 
  
  
Column 
(a) 
  
When the result of ANN classifier is analysed from Figure 3, 
two points draw attention. The first is related to larger number 
of bare soil pixels in the image compared to the ML result. The 
analysis of ground truth data showed that ANN classifier 
produced more realistic results for this class, as well. The 
second problem is about the incorrect classification of sea 
pixels as inland water for pixels along the seashore. This is 
most likely resulted from the ground truth image due to the 
possibility of selected pixels of inland water and water 
reservoirs being sand or rock. Therefore, the pixels assigned to 
inland water class along the seashore could be representing 
sand or rock. 
5. CONCLUSIONS 
The degree of contributions of multi-temporal image data and 
their principal components in the production of a classified 
thematic map is investigated in this study. The classification 
methods considered are the Maximum Likelihood (ML) and 
Artificial Neural Network (ANN) classifiers, sophisticated 
methods that have been recently used for many investigations. 
The image data available for the study were from Landsat 
ETM+ and Terra ASTER images acquired in 2001 and 2002, 
respectively. The main objective was to use these images to 
determine the effect of principal components in the results in 
terms of the classification accuracy. ANNs were trained to 
learn the characteristics of the training sets prepared for 
combinations of images together with their principal 
components. Trained networks were later used in accuracy 
assessment and in the classification of subset image 
combinations. Several important results can be derived from 
the results. Firstly, the ANN classifier yielded more accurate 
results than the ML classifier. The robustness of the ANN can 
be easily seen from Table 5 and from the classified image 
  
Road 
assland 
   
  
Urban 
Deciduaus 
Coniferous 
Column 
(b) 
Figure 3. (a) Maximum Likelihood classification result, (b) Artificial neural network result 
UA 
Inland Water