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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
vidual will be eliminated as soon as possible, we will get a 
more effective algorithm. 
2.6 Mutation Operator 
Although the probability is very small, it plays an important 
role in preventing premature convergence and enhancing the 
algorithm’s global search capability. 
optimality. 
The aim of using partial perturbation tracking is to find a high 
convergence rate searching route. That is to say to increase or 
reduce some times of the step such as X t ±k*(X i -X'), so we 
can obtain some new candidates. The most sufficient one will 
be chosen as the final result. 
2.6.1 Adaptive Mutation Probability: In our algorithm the 
population’s mutation probability is determined by two 
variations: the difference of population genes and the 
individual’s mutation. It can be defined as follows: 
fo.01x£ m x(l-^)x( 7 ^) X -l^ x Mut n 
Mut„ 
Mut = 
Mut„ 
if £> Mut ■ 
J min 
others 
(12) 
Let’s put t as a temporal variable, dif as the population differ 
ence, ft(x) as the individual’s fitness, fit_ave as the average 
fitness, a, k m ( k m >0,a>0 )are adjustable parameters,, a con 
trol the degree of individual fitness which effects mutation 
probability. Usually it is 1, 0.5 or 2. Mut max is maximum mu 
tation probability, Mut mm is minimum mutation probability, t 
represents the current generation number, T is the maximum 
generation number. 
2.6.2 Custom Mutation Approach: As for the mutation 
operator, the step is very difficult to determine: small step is 
helpful in getting the global optimal solution, yet increases 
computing time; the big one consumes shorter time but is un 
able to guarantee the global optimal solution. This paper adds a 
partial perturbation tracking operation to the Multi-variable in 
homogeneous mutation operator for real-coding method which 
presented by Michalewicz, this method could determine the 
most appropriate mutation direction and enhance the search ef 
ficiency at the same time guarantee the result’s accuracy. Muta 
tion formula is as follows: 
X,+P ( , ) *(b-X l ) ifi=M< 0.5 
•X-P^to+X,) ifi=M< 0.5 P^rSd-t/Dt 
X i others 
(13) 
In the formula, a j , h t stands for the parameter boundary, r x ,r 2 as 
the random variable in (0,1), j means an random number, t is 
the current algebra, / represents current generation number, T 
is the maximum generation number, b is the attribute variation 
step of the curve shape factor which drops along with the gen 
eration. The step in this method has the features as follows: the 
bigger step in the initial evolution period is greatly advanta 
geous as enhancing the search efficiency, while the shorter one 
in the later evolution period when the population tends to stably 
and searches nearby the optimum value, can guarantee solution 
2.7 Selection Strategy 
In this paper, we use the synchronous selection strategy on 
multi-subpopulations. First, the original population is divided 
into two classes: “stable subpopulation” and “exploring sub 
population”. Different class has different evolution strategy and 
operators. Concretely, in the former we set a bigger probability 
of crossover and mutation, make full use of its exploring ability; 
In stable subpopulation avoiding excellent gene to be destroyed 
is our main purpose. In order to guarantee the population’s di 
versity, excellent individuals in the two subpopulations should 
be exchanged in every generation so they could share the ad 
vantages of each other. Individuals in Middle generation comes 
from the above two subpopulations, the most outstanding indi 
vidual in each population will be selected as candidate directly, 
the remains will be chosen by competitive selection method. 
For the sake of fusing the genetic characteristic of the two sub 
populations, middle population is endowed with small probabil 
ity of crossover and mutation. So that the best gene will be 
merged adequately and it is more likely to find new potential 
excellent genes. 
The synchronous selection strategy on multi-subpopulation is a 
eclectic method between avoiding premature and destroying 
excellent individuals. Simultaneously the optimal gene reserved 
strategy and the optimal individual reserved strategy are 
merged properly. 
2.8 Evolution Control Parameters: 
Population Size: Usually it is a constant and even number. 
Crossover Probability: reference to section 2.5, usually it is not 
more than 0.75; 
Mutation Probability: reference to section 2.6, usually it does 
not exceed 0.05; 
Generation Number: In view of the population evolution and 
the algorithm’s efficiency, generally set it around 100 genera 
tions 
Termination Criteria: if any of the following conditions are met, 
terminating the algorithm. 
1) The population difference is smaller than thresh 
old. 
2) The best individual is sufficient to meet the fitness 
requirements. 
3) Achieved maximum evolution algebra. 
3. MORPHOLOGY FILTER 
There are two important aspects in image processing using 
morphology: Morphological operation and Structural element 
chosen. Morphological open operation is good at removing 
bright details which is smaller than structure element and main-