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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
original image content information. These features are used for 
classification. In order to compute classification parameters and 
endemember selection the training sample with proper schema 
were introduced; also test sample for computing overall 
accuracy and classification assessment were picked. At the end, 
the image was classified by ML (figure 5) and LSU algorithms. 
The results of classification were sequence, rule images and 
fraction images which used for post classification. 
  
   
    
  
    
Figure 5. Maximum likelihood classification 
We implemented two algorithms of cellular learning automata 
and probability label relaxation. These two algorithms were 
used for post classification of the result of MLC and LSU 
classification which following results (table 1) for test samples 
were obtained. 
  
  
  
  
  
  
Classification ML LSU 
Algorithm 
Post Without Without 
processing post PLR| CA post PLR | CA 
algorithm process process 
Overs 68.01 |72.2| 84,3 | 74.40 | 783 | 87.5 
accuracy 
  
  
  
  
  
  
Table 1. The result of post processing algorithm 
Noticing to the result, it could be realised that the accuracy of 
images was increased, and the cellular learning automata 
adapted itself better to the environment as compared of 
probability relaxation algorithm. Another interesting result is 
that the result of CLA for two classification algorithms is close 
together. Therefore it could be said that the CLA could 
overcome the result of poor classification such as MLC in 
Hyperspectral images. And also CLA could be used for transit 
fraction images computed by LSU to image classified and it is 
useful for accuracy assessment of sub pixel classifier. Another 
advantage of LA is that the CLA compensates the poor result of 
classification algorithm and it isn’t so sensitive to initial 
probability (state) but PLR is too sensitive to the initial 
probability. In addition the result of CLA algorithm is almost 
independent from initial probabilities and with respect to two 
parameters of entropy and omission error the CLA algorithm 
tries to optimize these parameters and to reach a global 
optimization. However in PLR it is possible to algorithm 
satisfied in local optimization. The two parameters of a and b in 
equation 11 affect to the results of post processing and it 
depends on our bias to local or global optimization. The 
prosperous of algorithm depends on schema which designs 
environment so active in which way. response actually to the 
action of automata and compute penalty and reward in a real 
Way. One of the disadvantage which experiments showed was 
995 
that the CLA takes more time as compared with PLR and we 
should control it by the number of iterations. 
7. CONCLOSION 
The algorithm developed is so flexible that can change label 
pixels to reach an agreement between neighbour pixels and 
decrease chaos in environment of image classified. Therefore 
cellular automata have good potential for dealing with problems 
which need to find the best choice until transiting from chaos 
environment to order environment. 
REFERENCES 
References from Books: 
Mather, paul M 1999. Computer processing of remotely-sensed 
images, john wiely& sons,pp.202-203 
Richards, A. John, 1993. Remote sensing digital image analysis, 
Third edition, Springer- Verlag Berlin Heidelberg, Printed in 
Germany, pp .196-201 
References from websites: 
Franciscus Johannes Maria van der Wel, 2000. Assessment and 
visualisation of uncertainty in remote sensing land cover 
classifications Faculteit Ruimtelijke Wetenschappen 
Universiteit Utrecht Netherlands 
www library.uu.nl/digiarchief/dip/diss/1903229/ref.pdf 
(accessed 20 sep. 2003) 
Fei Qian ,Yue Zhao Hironori Hirata, 2001. Learning Cellular 
Automata for Function Optimization Problems T.IEE Japan, 
Vol. 121-C, No.1. 
http://www.iee.or.jp/trans/pdf/2001/0101C 261.pdf 
(accessed 20 sep. 2003) 
Oommenl,B. John and T. Dale Roberts,2003 continuous 
learning automata solutions to the capacity assignment problem, 
School of Computer Science Carleton University Ottawa; 
http://www.scs.carleton.ca/-oommen/papers/CascaJn.PDF 
(accessed 20 sep. 2003) 
zur Erlangung des Grades, 1999. A New Information Fusion 
Method for Land-Use Classification Using High Resolution 
Satellite Imagery An Application in Landau, Germany 
Dissertation 
archimed.uni-mainz.de/pub/2000/0004/diss.pdf 
(accessed 20 sep. 2003)