ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001
106
The results were extremely accurate; the registration mismatch
was just a few pixels on average.
Figure 7 The reference pixels in the satellite image
25000
15000
-50 -20 10 40
Displacements (Pixel)
Figure 8 The results of voting scores
TABLE 1 The mean square errors yielded by proposed method
Region
1
2
3
4
5
MSE
2.17
1.91
2.23
1.57
1.48
Feature Pixels
181,582
80,864
92,069
65,451
48,343
Figure 9 The results of displaced map outlines
We also tested the approach of using image edges as matching
features to quantify the advantage of the NDVI approach. The
edges were extracted using the Robinson operator. Fig. 10
shows the edges extracted from the satellite image in Fig. 1.
Fig. 11 shows the voting scores using these edges. No clear
peak was detected in this case. Additionally, the mean square
errors are calculated using resulting displacements of our
approach. Table 2 shows the results of the 5 regions. The
number of feature pixels was about 910,500 per region on
average, and the average error was about 19.53 pixels 2 . There
are a large number of feature pixels, which include many pixels
unsuitable for voting. Therefore, the error is about ten times
worse. This fails to well determine mismatch. This is also the
reason for the large computation time. These results reveal that
the NDVI approach yields higher accuracy and smaller mean
square error than the approach of using image edges. It is clear
that our approach can extract effective features from satellite
images because it makes good use of topographical reference
objects in voting.
Figure 10 The results of edge extraction
-20 10
Displacements (Pixel)
Figure 11 The results of voting scores using edge extraction
TABLE 2 The mean square errors yielded by edge analysis
Region
1
2
3
4
5
MSE
16.26
16.32
29.49
16.84
18.77
Feature Pixels
1,360,398
692,913
1,218,203
668,366
612.646
