€————— z 
such sub-image pair, four patches were found, all 
correctly; in the second sub-image pair, eight patches 
were found with six being correct and the others being a 
neighbouring area of similar colour. The failures were 
due to the areas identified by the program as a patch 
not really being what we would regard as features. 
They were areas of vegetation of numerically uniform 
grayscale only slightly differing in colour from 
neighbouring areas, where the patch edge is 
non-existent to the eye, and imprecisely defined within 
the computer. 
We tested a second SPOT image pair captured six 
weeks apart, using a 500x500 section that contained no 
water bodies. Thirty patches were found to match by 
our algorithm, but of these, only seventeen were correct. 
Again, a large number of the patches were in areas that 
we would not identify as a feature, areas where 
gray-level gradually changed, giving patches with 
visually indistinct edges. Although better results would 
have been expected from the shorter elapsed time 
between capture of the two images, the lack of distinct 
uniform areas other than fields with slightly varying 
colour caused the large number of errors. 
Our present definition of what makes a patch is clearly 
inadequate. We have tried to improve it by requiring 
that the uniform area be surrounded by an edge, and 
using one of the many edge operators to locate the rapid 
change in gray-value. This has so far proved 
unworkable, because numerically defined edges are 
seldom continuous, and linking the segments possibly 
associated with a patch is not easy. 
More work needs to be done in this area. An avenue 
worth exploring is to require some proportion of the 
patch boundary pixels also to be edge pixels, by some 
definition. Points of high interest as indicated by the 
Moravec or Fórstner operators should be abundant on 
clearly visible patches. This should eliminate the 
within-field patches which are the main source of error 
at this stage of development. 
3. From patches to points 
The proof of the viability of a patch-based method for 
automating registration of a pair of images will be the 
success with which matching points can be found in the 
images, from the patches. Although we could consider 
the centre or centroid of the patch as such a point, any 
vagueness in the location of the patch boundary will 
translate into uncertainty in the position of the centre. 
A better approach will be to look for significant points 
along the boundary of the patch and systematically 
match them. 
In the testing we have done, we have selected all 
boundary points as worth searching for. We select a 
point on the boundary of a patch in the left image, and 
search for its match in the right image, using a search 
window. This window is centred on a point in a roughly 
corresponding place, related to the centroids of the 
patches. We had to choose between using a 
least-squares iterative method (Ackermann, 1984; 
Gruen, 1985) and a correlation method to find the 
matching point (eg, Barnard and Thompson, 1980). We 
chose to use a correlation method, because we felt that 
the alternative least squares method might be too 
susceptible to failure in the initial stages when the 
geometry of the matches had not been determined with 
much precision, even though it would probably be faster 
and may give better accuracy. 
Under our chosen method, the correlation between the 
selected point in the left image and all points in the 
related search area are calculated. The point in the 
search area with the highest correlation with the 
selected point is chosen, provided that this correlation 
is greater than some specified threshold. The match is 
subsequently refined to achieve sub-pixel accuracy. 
We need to select the size of the search window, and the 
size of the area around the point upon which the 
correlation calculation is to be based. Although sizes of 
correlation areas ranging from 3x3 to 27x27 have been 
used by other workers, Shirai (1986) showed that large 
windows were generally suitable for obtaining global 
range information, but gave smooth changes in 
correlation with a broad minimum around the 
corresponding point, and consequent imprecision in the 
match. Small windows gave a sharper minimum at the 
corresponding point, but were more sensitive to noise. 
We conducted our own tests, with the results being 
checked carefully by hand to find the success in each 
case: 
  
  
Correlation Number of Number 
area corresponding points correct 
found 
3x3 393 90 
5x5 362 171 
7x7 360 242 
9x9 342 195 
  
  
  
As can be seen the best results were achieved with the 
7x7 correlation area, which we adopted. 
The size of the search window will directly influence 
calculation time and success. We tested two sized 
areas, 11x11 and 23x23. We found the smaller area to 
be quite adequate, with no fewer correctly matching 
points, and with fewer ambiguities where more than 
one point had a high correlation. 
3.1 Sub-pixel accuracy 
The correlation method as just described should find a 
match with an accuracy of one pixel. In practice, the 
actual point will more likely be somewhere around this 
pixel, since the centre of each one of the corresponding 
pixels usually is not the image projection of the same 
point in the true object. To get to the point, a window of 
size 3x3 centred on the corresponding point is chosen. 
Then, a new coordinate value is calculated as a 
weighted average of the pixel’s coordinates in the 
window, using as weight the correlation value for each 
pixel in the window: 
= SS. Ui Xi 
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B2. Vienna 1996 
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