ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
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made objects possible. Note that there is a limit as a size of
object can be discriminated, which depends on a resolution of
the satellite image.
Figure 2 An example of 1/2,500 scale map
Figure 3 An example of red channel image
Figure 4 An example of near infrared channel image
Figure 5 The results of NDVI calculation
Figure 6 The extracted feature pixels
Following the procedures described in section 3, mismatch was
determined using the resulting feature pixels To simplify the
discussion, only the results of y-direction displacement are
shown below. Fig. 7 shows the resulting reference pixels in the
satellite image; this was realized by projecting map objects onto
the image using known coordinates. This figure shows that the
projected positions are somewhat shifted against the real object
positions shown in the satellite image. Fig. 8 shows that the
voting scores exhibit one clear peak. 64,251 votes were cast in
this case. The displacement with the lowest mean square error
is -13 pixels in this case. This value is equivalent to about 52
meter mismatch. The mean square error is also 1.57 pixels 2 .
Similarly, the registration results are compared in Fig. 9 to the
original projection shown in Fig. 7. The figure shows that the
registration of this image has slight errors. The results show
that our approach is very effective in suppressing many of the
errors common in mismatch determination.
To quantify the average performance of the proposed approach,
the results of 5 regions (including the above area) are shown in
Table 1. The size of each region is approximately 500 meters x
500 meters. In this case, the number of feature pixels was
about 93,600 per region on average. The average error was
about 1.87 pixels 2 . We confirmed by projecting the maps over
the satellite images that the registration has only slight error.
