be well suited for picking CCPs in satellite or aerial imagery 
in terms of speed and required accuracy [Luhmann and 
Altrogge 1986]. 
The Moravec operator locates points with high variances in 
all directions by calculating the square of grey value 
differences in the four principal directions. If the minimum 
of the four squared differences, called the interest value 
(IV), exceeds a given threshold, a CCP is detected at the 
center of the operator's window: 
IV = min 
S( G i.j-°i.i + i) 2 
IXj-Gi*,/ 
X(Gi.j- G i+ , j +1 ) 2 
S(G, j -G i „. j . 1 ) 2 . 
CCP matching 
The next step in the registration process is the matching of 
CCPs between the reference and subject images. For each 
point in the reference image, there should be a corresponding 
one in the subject image. We currently use a "quick and 
dirty” algorithm to accomplish this via nearest neighbor and 
interest value comparison. 
The process is as follows: For each point in the reference 
CCP list, the CCP list from the subject image is scanned to 
find those points whose image coordinates are close to its 
coordinates ("closeness" is defined by a distance threshold 
input by the user) and also have a similar IV (again user 
defined by a threshold). If more than one point in the subject 
list is found, the one with the greatest IV is considered to be 
the corresponding point. Those points, in either list, which 
have not found matches are removed from further 
consideration. 
Where i = n-k...n+k, j = m-k...m+k, k = size of operator 
window, G n m is the pixel grey value at image coordinates 
n,m. 
It is desired that the interest operator should extract CCPs 
with a relatively even dispersal throughout the image in order 
to get the optimum rectification results. Unfortunately, the 
Moravec operator extracts points only in regions of highest 
image contrast. As a result, extracted points tend to lie in 
compact clusters in certain areas of the image - leaving other 
areas nearly void of CCPs (figure la). 
To solve this problem, the Moravec operator is forced to 
extract points from low contrast areas. This is accomplished 
by partitioning the image into small subsets, then requiring 
the Moravec operator to extract a certain number of points 
from each. Only the points with the highest interest values 
are chosen. By partitioning, CCPs are selected relatively 
evenly throughout the image so that no part of the image is 
neglected (figure lb). 
+ + 
+ 
+ 
+ 
4 
+ + 
+ 
+ 
+ + 
+ 
+ 
+ 
+ 
+ 
+ 
: + 
? 
+ 
+ + 
+ 
4 
(b) Partitioned 
Figure 1. Image partitioning for CCP distribution 
As another refinement to the Moravec operator, those CCPs 
which are connected (i.e. adjacent) to a CCP with a greater 
interest value are also deleted from the list. Since the 
Moravec operator tends to select CCPs in connected groups 
of two or more, this helps avoid confusion and mismatches 
in the following correspondence operation. 
This approach to finding corresponding points has two 
inherent drawbacks: First, using nearest neighborhood will 
not work if there is too much rotational and/or translational 
difference between the two images from which the candidate 
point lists are drawn (more about this will be discussed 
later). Second, it is a one-way correspondence algorithm. 
Although each point in the reference list will only find one 
corresponding point in the subject list, the opposite is not 
necessarily true. There will most likely be several points in 
the subject list which have more than one corresponding 
point in the reference list. Note that this problem would be 
compounded if we did not eliminate CCPs extracted adjacent 
to each other in the previous step. 
Fine tuning the matched CCPs 
Once the CCP lists have been matched, it is then necessary 
to eliminate mismatches and determine more accurately the 
location of the corresponding CCPs in the subject image. A 
cross product moment correlation coefficient algorithm is 
employed for this task [Ehlers 1985]. If there is a high 
correlation of a reference window (placed about the CCP in 
the reference image) within a search window (placed about 
the corresponding CCP in the subject image), the CCP in the 
subject image is moved to the location within the search 
window with the highest coefficient. This location is 
calculated to subpixel accuracy, which may be necessary in 
order to create a sufficiently accurate registration. 
If the coefficient is less than a user-defined threshold, the 
corresponding points are considered to be a false match and 
are deleted. In addition, the fine tuning process also 
eliminates many cases where a single subject image CCP is 
matched to more than one reference image CCP. Each 
reference point, when correlated, would most likely move 
the same corresponding point to different locations, creating 
two separate subject image CCPs from one and removing the 
original ambiguity. 
THE "LA GRANGE" SOFTWARE PACKAGE 
A prototype image matching software package, called "La 
Grange," has been written in FORTRAN 77 and 
implemented on a Dell 386 microcomputer in the ERDAS 
v7.3 environment. It incorporates steps 2 through 4 in the 
matching process. The remaining steps are performed using 
existing ERDAS image processing software (figure 2). 
446