Initially, two tests were done to establish the weights to be used, for the scale and shape parameter, in the multi-resolution segmentation algorithm. The results, as indicated by figure 14 and 15 show that the accuracy of the objects decreasing as the shape parameter is increased; this is due to the increase in the size of the objects. The optimum Shape value of 0.25 was selected; smaller values resulted in very small objects that were not practical. Using the best Shape settings, the results for the Scale parameter tests indicated that as the scale value increases, the accuracy decreases. An optimum Scale value of 0 was selected with a compactness of 0.
Figure
indicated a decreasing accuracy as Shape weight increases
Figure 16
the objects decrease as
Figure 16 is a visual segmentation weight values. are shown in table 6.
as the weights increase, is shown in figure 1 of the image objects created.
Table 6: Scale:0) Cairo Delhi Dhaka Lagos Shanghai
Figure 15: The accuracy of image objects, using different Shape weights values. The results
cated a decreasing accuracy as Shape weight increases
16: The results from testing different weight settings for image segmentation. The accuracy of
the objects decrease as
Figure 16 is a visual segmentation weight values. are shown in table 6.
he weights increase, is shown in figure 1 of the image objects created.
: The accuracy of the image objects created, using the
Scale:0) 1990 95.50% 94.00% 92.00% 92.00% (1984) Shanghai 95.00%
The accuracy of image objects, using different Shape weights values. The results cated a decreasing accuracy as Shape weight increases
The results from testing different weight settings for image segmentation. The accuracy of the objects decrease as the weight
Figure 16 is a visual representation segmentation weight values.
are shown in table 6. An example of the decreasing accuracy of the image objects, he weights increase, is shown in figure 1
of the image objects created.
The accuracy of the image objects created, using the
1995 92.00% 98.00% 93.20% 94.00%
The accuracy of image objects, using different Shape weights values. The results cated a decreasing accuracy as Shape weight increases
The results from testing different weight settings for image segmentation. The accuracy of the weight values set for the Scale parameter is
representation
segmentation weight values. The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects, he weights increase, is shown in figure 1
of the image objects created.
The accuracy of the image objects created, using the
2000 89.90% 89.90% 89.00% 87.00% 92.00% 46
The accuracy of image objects, using different Shape weights values. The results cated a decreasing accuracy as Shape weight increases
The results from testing different weight settings for image segmentation. The accuracy of values set for the Scale parameter is
representation of the results from testing the various The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects, he weights increase, is shown in figure 16. Figure 1
The accuracy of the image objects created, using the
2005 91.10% 94.00% 89.80% 91.70% 89.50% 92.00% 89.00% 92.00% 95.00% 92
The accuracy of image objects, using different Shape weights values. The results cated a decreasing accuracy as Shape weight increases
The results from testing different weight settings for image segmentation. The accuracy of values set for the Scale parameter is
of the results from testing the various The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects,
Figure 17, 18
The accuracy of the image objects created, using the optimum
2010 2015 94.00% 92.00% 91.70% 91.00% 92.00% 92.00% 92.00% 87.40% 92.50% 87.00%
The accuracy of image objects, using different Shape weights values. The results
The results from testing different weight settings for image segmentation. The accuracy of values set for the Scale parameter is increased
of the results from testing the various The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects, 7, 18 and 19 shows examples
optimum weight settings
2015 92.00% 91.00% 92.00% 87.40% 87.00%
The accuracy of image objects, using different Shape weights values. The results
The results from testing different weight settings for image segmentation. The accuracy of increased
of the results from testing the various The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects, 19 shows examples
weight settings (Shape:0.25, The accuracy of image objects, using different Shape weights values. The results
The results from testing different weight settings for image segmentation. The accuracy of
of the results from testing the various The object accuracy test results for each year by city An example of the decreasing accuracy of the image objects, 19 shows examples
Figure
segmentation process. As shape (left) increase the object contain mixtures of land use types. As the Scale (right) increases th
at scale 0.5 the objects start to contain a mixture of land use types.
Figure 18
(Landsat 7 ETM+, B=B1,G=B4, R=B3)
Figure 17: An example of the decreasing accuracy of the image objects created during the
segmentation process. As shape (left) increase the object contain mixtures of land use types. As the Scale (right) increases th
at scale 0.5 the objects start to contain a mixture of land use types.
18: An example from an elongated image object created for a section o
(Landsat 7 ETM+, B=B1,G=B4, R=B3)
An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the Scale (right) increases the object size increases, at Scale 0 the objects as the size of individual pixels, at scale 0.5 the objects start to contain a mixture of land use types.
n example from an elongated image object created for a section o (Landsat 7 ETM+, B=B1,G=B4, R=B3)
An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels, at scale 0.5 the objects start to contain a mixture of land use types.
n example from an elongated image object created for a section o (Landsat 7 ETM+, B=B1,G=B4, R=B3)
47
An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels, at scale 0.5 the objects start to contain a mixture of land use types.
n example from an elongated image object created for a section o
An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels, at scale 0.5 the objects start to contain a mixture of land use types.
n example from an elongated image object created for a section o
An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels,
n example from an elongated image object created for a section of road in Cairo, Egypt. An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels,
f road in Cairo, Egypt. An example of the decreasing accuracy of the image objects created during the segmentation process. As shape (left) increase the object contain mixtures of land use types. As the e object size increases, at Scale 0 the objects as the size of individual pixels,
Figure 19
5 TM, B=B1, G=B3 ,R=B2)
Figure 20
(Landsat 5 TM, B=B1, G=B3, R=B2)
19: An example of image objects created
5 TM, B=B1, G=B3 ,R=B2)
20: An example
(Landsat 5 TM, B=B1, G=B3, R=B2)
n example of image objects created 5 TM, B=B1, G=B3 ,R=B2)
n example of image objects created (Landsat 5 TM, B=B1, G=B3, R=B2)
n example of image objects created
of image objects created (Landsat 5 TM, B=B1, G=B3, R=B2)
48
n example of image objects created along the Huangpu River,
of image objects created on the edge of the urban along the Huangpu River,
on the edge of the urban
along the Huangpu River, Shanghai, China (Landsat
on the edge of the urban areas of
Shanghai, China (Landsat
areas of Shanghai, China Shanghai, China (Landsat