Operator is a comer detector since it defines interest points as 
points where there is a large intensity variation in every 
direction. Directional variance is measured over small square 
windows. Sums of squares of differences of pixels adjacent in 
each of four directions (horizontal, vertical and two diagonals) 
over each window are calculated, and the window's interest 
measure is the minimum of these four sums. Features are 
chosen where the interest measure has local maxima. The 
feature is conceptually the point at the center of the window 
with this locally maximal value [7]. 
2.2 Feature point matching 
In this step, the correspondence between the feature point 
detected in the sensed image and those points in the reference 
image is established. This step is the key of the image 
registration. With many highly matching precise control points, 
we can calculate the relation of the two coordinates system of 
the images on the model we proposed with what the 
registration will be done. There five key step in this part. 
2.2.1 General match: For enhance the matching precise and 
efficiency. We should estimate the approximate space of each 
feature point. As you know, search the corresponding point all 
over the slave image is impossible. So with general match, we 
can limit the search area in a small window. In this paper, we 
choose 1~3 artificial tie points with which we can obtain the 
parallel move, rotate and zoom of the two images. If the tie 
points are more than three, we can use affine model. With the 
parameters we got, we can cast the feature points on the slave 
images. So we just need to search the corresponding point 
round the points which been cast. 
J(S a -S i t /N)(S,,-S 2 t -/N) 
(1) 
in which 
m n 
Sg' ~ ^ i ^ 1 & i+rj+c 
i=l y=l 
Sgg' ~ ^ 1 ^ 1 &i,j * & ,+r J+ c 
/=1 7=1 
m n 
S g'g' = S' 1 ' l y.s' 2 ‘+r,j+c 
<■=1 7=1 
m n 
<=1 7=1 
m n 
s gg = X2 g2<j 
1=1 7=1 
m,n is the template line length and row length. N=m*n, This 
statistical measure has the property that it measures correlation 
on an absolute scale ranging from [-1,1]. The images which are 
more similar with each other will have a large correlation. 
B . Mutual Information 
2.2.2 Pyramid-layered template matching technique: 
Though using general matching, there are still some distance 
from the correct corresponding point. If using large range 
search method, not only the efficiency would be very low, but 
also very hard to get good match result. We solved this math 
problem perfectly by using the pyramid-layered template 
matching technique. The pyramid-layered template matching 
method is a match method that is related from top and bottom. 
This correlation method is a strategy that is from coarse to 
precise. It gained high match reliability by using the correlation 
from the top of the pyramid-image. At the same time it gained 
high precision by the correlation from the bottom of the 
pyramid-layer. 
The number of the pyramid-layer is determined by the deviation 
between the initial predicting pixel position and the actual pixel 
position. Where, the bottom of the pyramid-image is the 
original aviation image. The top of the pyramid-image is 
formed by put together 3X3 pixels from the bottom of the 
pyramid-image. By using this sequence image correlation 
method, we could calculate the correct point position form 
coarse to precise. 
2.2.3 Similarity metrics: In the points matching process, 
one of the most important things is to choose a similarity 
metrics. Correlation and Mutual Information are the most 
widely used. 
A. Correlation 
Correlation can be used in multi-source remote sensing images 
[11] which defined as 
The strength of the mutual information similarity measure lies 
in tile fact that no assumptions are made regarding the nature of 
the relation between the images intensities in both modalities, 
except that such a relationship exists. Therefore, the MI 
criterion is very general and has been used in many different 
image registration problems [3-6]. 
Unlike the metrics of the mean-square difference and cross 
correlation coefficient, mutual information based algorithms do 
not assume any functional relationship between the intensities 
of the images to be registered: thus, they are more general, 
effective and robust to handle the images when intensities of 
one image have a nonlinear relationship with those of the other 
image. 
Mutual information has its roots in information theory. The 
mutual information (MI) of two random variables A and B is 
defined by 
P AB (a,b) 
P A {a).P B {b) 
(2) 
Where and P ii( b ) 
are the marginal probability mass 
functions, and A.B( a > ) j s the j 0 i n t probability mass 
function. MI measures the degree of dependence of A and B by 
measuring the distance between the joint 
distribution 
P a.b (°> b ) 
and the distribution associated with the