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It was observed that different weights should be given to the
differences between the parameters that characterise the
polygon shape. More precisely, it had to be considered that
the difference in area of the polygons to be matched had to be
scaled to at least to half of its value since it was adversely
affecting the matching results. This problem can be easily
understood by considering that the area of the polygon
includes all the pixels inside the boundary of the polygon,
depending on the size of the polygon a slight difference in
shape can contribute to a high difference in the area. If not
reduced the difference in area between the polygons would be
the main characteristic to be considered when adding all the
differences between the different parameters used to
characterise the polygons. Taking this into account the
matching was performed with no problems. For each polygon
in the map one polygon in the image was found whose
characteristics were considered to be the most similar. The
resulting value from adding all the differences between the
parameters of each pair of polygons was then divided by the
perimeter of the polygon to compensate for the fact that more
differences are expected to occur on polygons with a longer
perimeter. It was observed that to eliminate the bad map to
image correspondences the matching value for each match did
not provide a very robust way of identifying the bad matches.
To solve this problem, it was also necessary to consider the
relative geometry of the patches to detect the wrongly
matched patches. Two approaches were considered to
perform the selection of the good matches. The first method
consists of computing geometric relations between the map
polygons, such as distance and angles, and then checking that
the corresponding image polygons maintain similar geometric
characteristics among them. The second technique starts by
considering that the best matching value should belong to a
correct match. Based on this, a transformation is computed
between the best matched polygons using the centre of
gravity, and the furthest and the closest point from the centre
of gravity of both map and image corresponding polygons.
The parameters found by this transformation are then applied
to transform each map polygon centre of gravity into the
corresponding image polygon centre of gravity. The
transformed centre of gravity is then compared with the
centre of gravity of the image polygon that is considered to
match the map polygon under consideration. If these two
values coincide then the matching is accepted, if they are
different then this means that the original correspondence is
wrong and that map and image polygons do not match. Both
techniques were implemented. It was observed that the
second approach can cause problems because the map to
image transformation initially used focuses on a restricted
area of the image where the polygon that starts the process is
located, this may be quite different from the transformation to
. be used on polygons distributed in the different areas of
image. Therefore the first procedure to filter the bad matches
was adopted since it applies extra geometric constrains that
assure a correct result. Table 1 shows the matching values,
the values highlighted represent the good matches.
Once the common polygons are identified a refinement of
the matching and a consequent identification of conjugate
points can be performed using the map to image matching
technique based on the dynamic programming algorithm
described in section 2.2. Now that the conjugate polygons
have been found using the map to image matching algorithm
based on shape, the knowledge of the centre of gravity of
these polygons together with the width and height of the
bounding rectangle can be used to section the original image
into areas only containing the polygon to be further
processed. The resolution of the image and the map for this
matching is 2m per pixel which corresponds to the minimum
resolution to be used with a 1:10 000 map. Using the method
outlined in section 2.2, the initial estimation of the
transformation between the map and the image can be
computed.
Table 1 Isle of Wight matching values and corresponding
matched polygons
Matching Value | Map Polygon | Image Polygon |
0.629 1 6
1.641 2 6
1.476 3 17
1.826 4 13
0.810 5 8
1.370 6 20
0.839 7 10
0.660 8 10
1.491 9 9
1.644 10 18
1.780 11 4
0.623 12 18
0.555 13 13
2.778 14 18
1.008 15 18
1.284 16 18
1.846 17 18
2.498 18 6
1.463 . 19 18
Figure 5 shows one of the map polygons with the respective
image polygon used. These two polygons were
independently processed using the techniques described in
section 2.2 in order to prepare them to be matched. The
points matched by the dynamic programming are also shown.
4. REGISTRATION OF LANDSAT THEMATIC
MAPPER DATA USING FOREST AREAS
The basic method is described in Newton et al (1994). The
objective of this work was to detect change in forest and the
strategy adopted was to manually identify corresponding
forest areas and then to use the dynamic programming routine
to match the edges of the polygons and to detect any changes
between the map and the image. A major part of this work
was to identify and classify the forest using multispectral
data. Landsat Thematic Mapper data was used and after
classification the forest area was segmented using a disc
filling routine and the edges extracted. The corresponding
forest is found from the green layer in 1:50 000 vector data.
The boundary matching of Thematic Mapper data was tested
by Holmes (1994) by selecting control points along the
boundary and determining the parameters of an affine
transformation. The root mean square error on 4 points was
0.46 pixels in plan and inspection showed a distribution of
residuals which was consistent with an affine transformation
but which could be corrected with a plane projective
transformation. The assessment also showed that the method
would not be entirely reliable when complex edges were
involved. It was found that the relief differences over the TM
image, although quite large would not affect the accuracy of
the boundary determination.
In this part of the work only the boundary matching is
automatic and the output is a set of two dimensional
conjugate points.
143
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996
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