matching window and for different levels of the pyramid 
images. If the approximate range of elevations in the 
overlapping area is already known, it is easy to derive a 
searching range for different cases. Otherwise approximate 
geometric transformation parameters can be used to derive it. 
The searching range should decrease with increasing the 
level of pyramid images when we consider image resolution. 
On the other hand, when we consider the matching strategy 
from the top level down to the original image, i.e., acquired 
approximations are getting better from the top level down, 
does the searching range decrease with decreasing the level. 
Generally, we define the searching range as large as possible 
on the top level of the pyramid images to avoid the 
possibility of losing matching. 
3. DISCUSSION 
In this section some characteristics of the RG-DW 
matching scheme are discussed. 
3.1 Simplicity 
As described above, the RG-DW scheme combines the 
advantages of very simple elementary tools. Without 
introducing any complicated concepts and algorithms, and 
without any a priori knowledge about images and object 
space, the RG-DW can be easily programmed and applied to 
match satellite images and aerial photos. 
3.2 Reliability 
For each anchor point a total of at least 12 windows of 
different sizes are used to do the matchin g. They are 
compared to each other, so that the percentage of wrong or 
lost matches drops dramatically compared to the case where 
only a single window is used to do the matching. 
3.3 Accuracy 
Many experiments with the RG-DW show that the 
matching accuracy is very satisfactory, therefore the results 
can certainly be used as very good approximations of a 
successive least squares matching to get subpixel accuracy. 
3.4 Independence 
As mentioned before, the RG-DW does not rely on any a 
priori knowledge about image orientation or information 
about object space. The only thing to do before performing 
matching is to very roughly determine the four corner points 
at the overlapping area of the stereopair of images. The 
whole matching process can be completed independent of 
other processes such as image orientation. In other words, 
once a stereopair of digital images is on hand, matching with 
MATCH can be implemented. On the other hand, 
approximations for all points to be matched can be derived 
from the first geometric transformation parameters, i.e., each 
point can be processed independently. 
3.5 Parallelism 
It is clear that this characteristic directly comes from the 
independence of the RG-DW noted above. Because the 
required initial values for cross-correlation of each point are 
directly derived from the geometric transformation 
parameters between the two images, it is very convenient to 
perform a parallel processing, i.e., all points to be matched 
can be derived at the same time, if array processors or 
parallel computers are available. 
On the other hand due to this parallelism, systematic 
matching errors are greatly eliminated. 
4. RESULTS 
The software package MATCH based on the RG-DW is 
the kernel of a new commercial digital photogrammetry 
module, the Digital Ortho Module of ERDAS Inc.. It has 
been applied for both SPOT images and aerial photos. The 
success rate of matching results (SRMR) is quite high. For 
two pairs of full SPOT (1A) success matching was 
performed at different grid intervals. For the case with an 
815 
interval of 240 pixels, 484 (22 x 22) anchor points were 
matched, which are homogeneously distributed over the 
whole overlapping area. The point were visually checked on 
the screen of a Sunsparc 1 workstation with full image 
resolution. The SRMRs of both pairs are 96 percent. Most 
wrongly matched points fell into clouds or empty areas 
without information. Figures 3 and 4 show one of the pairs. 
Figure 3 is the pair of original images with 6000 x 6000 
pixels and figure 4 shows the anchor point matching results. 
The SRMR for densification is similar as the anchor points 
according to the visual color examination method mentioned 
above. If those anchor points with lower reliability indices 
values had been interactively corrected, the SRMR of 
successive matching would increase. Figure 5 shows 
densification matching points in a small area (512 x 512 
pixels) of figure 3. It can be seen that even though there 
exists quite a big geometric distortion, the matching results 
with the RG-DW are still good. Figure 6 is a DEM created 
from the matching results of figure 3. Figure 7 is a 
perspective view derived from figure 6. 
Figures 8 to 12 are results of aerial photos digitized with 
a pixel size of 100 microns. Figure 8 shows a pair of 
original images and some anchor points. Figure 9 and 10 
display two parts of densification matching results (512 x 
512 pixels). Figure 11 is the DEM derived from the 
densification matching results of the overlapping area. 
Figure 12 is a perspective view of the DEM overlaid with the 
orthophoto. Figure 13 shows matching results from aerial 
photos taken by a CCD camera installed in a helicopter. 
It should be noted that all results displayed here are direct 
results without any correction by the human operator, so 
some inconsistent situations may be found in the figures. 
For visibility, the sizes of crosses representing matched 
points in the images were enlarged. 
Although the authors did not optimize the program, it 
is acceptable for current use, for example, the run time of 
MATCH for the full scene of figure 3 with intervals of five 
pixels on a Sunsparc 2 was about 24 hours. 
5. OUTLOOK 
The RG-DW scheme with its simplicity, reliability and 
parallelism, and its practical application shows that it is a 
promising image matching technique. Further investigations 
will be concentrated on the following aspects: 
1. Postprocessing - make use of some existing 
conditions to automatically detect and delete Wrong 
matches. 
2. Investigate and design parallel algorithms with the 
RG-DW. 
REFERENCES 
Ackermann, F., Hahn,M., 1991: Image Pyramid for Digital 
Photogrammetry, Proceedings of the 43rd 
Photogrammetric Week, Heft 15, Stuttgart 
Lue, Y.and K. Novak, 1991: Recursive Grid-Dynamic 
Window matching for automatic DEM generation, 
Proceedings of 1991 GIS/LIS and ACSM-ASPRS Fall 
Convention, pp. A254-260, Atlanta 
ERDAS Inc., 1992: Field Guide of ERDAS 
Lue, Y.and K. Novak, 1992: An Operational Image 
Matching Package «MATCH», Proceedings of 1992 
ACSM-ASPRS Annual Convention, Washington D.C. 
Novak, K., 1992: Application of Digital Cameras and GPS 
for Aerial Photogrammetric Mapping, Presented paper 
of Commission I of XVII ISPRS Congress, 
Washington D.C.