inbul 2004
sition were
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Table 1. Results using least-squares matching
Coordinates Coordinates Affine Discrepancies Projective Discrepancies
(master) (slave) ransformation (pixels) transformation (pixels)
1 (381, 220) (379, 238) (379.6, 238.4) 0.69 (379.2, 238.0) 0.24
2 (205, 350) (205, 369) (206.7, 367.7) 2.10 (208.3, 367.6) 3.62
3 (91,285) (91, 304) (94.9, 303.8) 3.88 (93.7. 303.8) 2.73
4 (112, 1073 (111, 125) (110 2,123 1) 2.07 (109.7, 123.4) 2.04
5 (246, 39) (237,55) (237.1. 35.6) 0.65 (237.1, 56.6) 1.61
Average 1.88 : 2.05
Table 2. Results using cross correlation matching
Coordinates Coordinates Affine Discrepancies Projective Discrepancies
(master) (slave) ransformation (pixels) transformation (pixels)
I (381, 220) (379, 238) (370.1. 233.3) 0.33 (370.0. 238.2) 0.22
2 (205, 350) (205, 369) (205.3, 368.7) 0.48 (205.0, 369.0) 0.00
3 (91, 285) (91, 304) (91.0, 304.0) 0.00 (91.0, 303.8) 0.24
4 (112. 107) (111,125) (110.5, 125.1) 0.54 (110.4, 125.2) 0.64
S (246, 39) (237, 55) (237.5, 54.9) 0.51 (237.2. 53.9) 1.10
Average 0.38 0.44
The experimental results are summarized in Tables | and 2 for
the example shown in Figure 4.
The results obtained using the cross correlation matching, all of
which accomplished sub pixel accuracy, are better than those
achieved using least-squares matching. It was initially assumed
that projective function would result in the best improvement,
however affine transformation was more effective in some cases.
S. CONCLUSIONS
The conclusions of this paper are as follows:
e An improvement to the standard method of least-squares
matching is possible, in regard to optimization.
* The relationship between least-squares matching and
cross correlation matching has been confirmed.
* Cross correlation matching can be formulated to include
image deformations.
e An experimental comparison between least-squares
matching and cross correlation matching by using
IKONOS stereo imagery has been demonstrated.
Geometrically constrained matching could be expected to
further improve accuracy, though this paper has not dealt with
geometric constraints. ^ Nevertheless, the cross correlation
matching yielded positive results. It indicated that the method
has potential to be a powerful matching strategy.
Verification of the stability of the method needs to be further
investigated by applying various conditions (illumination and
location, including central city with high buildings, suburbs,
mountain area, etc.). So far, this application has been relatively
restricted.
As mentioned above, our final goal is to make a contribution to
the matching of multi-temporal and multi-resolution images,
including IKONOS, QuickBird and aerial imagery. It will
require a combination with other techniques, especially those
related to change detection and resampling.
605
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