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# Full text

Title
Proceedings of the Symposium on Global and Environmental Monitoring

Each valley segment is traced in turn by searching
for logical extensions of the segment in a
direction consistent with the current direction of
the segment. The tracing stops when there are no
more pixels to add to the segment, or when the
segment encounters a pixel labelled as part of
another valley. In this case, the pixel which has
been encountered is labelled as a node. All node
locations are stored in a file for the matching
process.
In the examples presented here, there were 367
nodes and 945 valley segments detected in the SAR
mask and 2813 nodes and 4731 valley segments
POINT PATTERN MATCHING
The goal of this step is to identify a subset of
nodes from each of the masks which correspond to
the same location on the ground. The method is
loosely based on the relaxation algorithm proposed
Given a hypothetical transformation between the
two masks, we may examine all combinations of
point pairs (hereafter a 'point pair' implies one
node taken from each image) and determine whether
or not the pair 'supports' the hypothesis. The
support is calculated by a figure of merit defined
by:
4>(x) = (1 +(^ L ) 2 )' 1
where x is the Euclidean distance between the test
point pair after applying the transformation. It
can be seen that as x increases, the figure of
merit decreases. The parameter a is included to
allow for an error tolerance in the match.
The algorithm used here operates as follows. Every
possible combination of point pairs is tested as
a hypothetical match with each match defining a
hypothetical linear translation between images. An
MxN 'support matrix' can thus be defined, where M
and N are the number of points in each mask to be
matched. The support for each hypothesis is
calculated by searching all remaining combinations
of point pairs to find an optimum set of pairs,
ie. the pairs with the least separation after
applying the hypothetical translation. The figure
of merit is calculated for each pair in the
optimum set and summed.
The support matrix is then thresholded at a fixed
percentage of the maximum support value in the
matrix. The point pairs which give a support value
below the threshold are discarded and the calcula
tion of the support matrix is repeated using the
subset of points which remain after thresholding.
The process is iterated until a consistent set of
point pairs remains, all of which give support
values above the threshold.
Since the calculation of the support matrix takes
on the order of 0(M 2 N 2 ), the number of points used
must be minimized. This is accomplished automati
cally by thinning the initial set of points by
retaining only those nodes which are connected to
long valley segments (since these are most likely
to be included in both masks) and by deleting
points which are close to one another in the same
mask using valley length information stored at the
are also eliminated at each iteration, and there
fore the calculation takes less time as it
progresses.
The resulting set of point pairs may then be used
to define an affine transformation model. The
original lists of nodes are searched to find
additional point pairs which match within the
error tolerance based on this new transformation
model.
The success of the point pattern matching
algorithm depends on having a sufficient number of
points in each mask which actually correspond. The
low resolution DEM must be sampled at the same
resolution as the SAR image resolution to give a
similar level of detail in the valley network.
Given this condition, the method is robust with
respect to the choice of the processing
parameters, a and the support threshold.
Figure 3 shows the DEM valley mask overlaid on the
SAR mask, resampled using the affine transforma
tion model which results from the match. In this
example, the initial thinning parameters were a
minimum valley length of 5 pixels in the SAR mask
and 15 pixels in the DEM mask, and a minimum node
separation of 5 pixels, leaving approximately 150
nodes from each mask to be used as input to the
matching process. A consistent set of 8 point
pairs were successfully matched at the end of the
iterative process, using a=3 pixels and a
threshold of 65%. An additional 51 point pairs
were added using the affine transformation model,
resulting in an RMS error of approximately 1 pixel
(50m) in both the pixel and line directions.
CONCLUSIONS
A feature-based image matching algorithm for
ground control point acquisition has been
presented. In the application shown here, valley
masks extracted by two different methods have been
successfully matched. The algorithm can easily be
adapted for other applications where a spatial
point pattern of image features may be extracted
from two images. The only requirements are for the
two images to be roughly similar in scaling and
rotation, and have a similar level of detail in
the features extracted.
REFERENCES
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Shading Technique for the Extraction of
Topographic Models from Individual Spaceborne SAR
Images. Proceedings of the 1989 International
Geoscience and Remote Sensing Symposium. 597-602.
Guindon, B. and H.Maruyama, 1986. Automated
Matching of Real and Simulated SAR Imagery as a
tool for Ground Control Point Acquisition. Can.
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Qian, J., R.W.Ehrich and J.B.Campbell, 1990.
DNESYS - An Expert system for Automatic Extraction
of Drainage Networks from Digital Elevation Data.
IEEE Trans. Geoscience and Remote Sensing. 28:29-
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Ranade, S. and A.Rosenfeld, 1980. Point Pattern
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