The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
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Beside genetically guided road key point identification, the idea
of ellipse clustering was developed to another novel method
called “increasing ellipse”. In this method, for each road patch
representative ellipse, the following parameters are determined:
Area. = 4 .a ; .b ;
Diff%, = Area -- N i
Sample% j
Area i
N i M
N
(3)
In this equation Diff% shows the percent of the i' h ellipse area
difference with the area covered by its associated road pixels.
Also Sample% is a measure of the expected assigned road
pixels to the i' h road patch.Based on the above introduced
ellipse parameters, road patches are categorized as Noise,
Coincide and Under-Evaluation patches.Noise patches are those
clusters having the following conditions:
(Diff%) Mean Dtff% % + 2.5<J Dlff% ) A ND(Sample%(0.5)
Sample%(0.1 (4)
b( 2
In equation 5, Mean Diffy an d C7 D ^ % are the mean and standard
deviation of Diff% i values of all M clusters.
Coincide patches are those clusters satisfying the conditions
expressed in equation 5.
The patch is not markes as Noise patch
Diff%(Threshold (5)
a < Ab
Under-Evaluation patches are the rest of clusters not marked as
noise nor as coincide patches.Figure 5 shows the methodology
of the invented increasing ellipse clustering.The termination
condition of this procedure is to have no ender-evaluation
patches while all of them are categorized as coincide or noise
patches
Figure 5. Increasing ellipse road vectorization
The termination condition of this procedure is to have no ender-
evaluation patches while all of them are categorized as coincide
or noise patches.
2.2.2 Road Key Point Connection: In order to make correct
connections between the identified road key points, the
presence of common road pixels between adjacent road patches
were used as the connection guide.
For this reason, a fuzzy shell clustering was implemented on the
pre-determined ellipse, representative of coincide patches.
Vague samples, which are road pixels belonging to more than
one road patch with rather the same membership values, were
determined based on the obtained membership matrix. Using
the centroid of vague road pixels as the middle point of key
points, the corresponding connections were generated.
3. PRACTICAL RESULTS
In order to evaluate the functionality of the road extraction
method proposed in this research, two sub-samples of pan-
sharpened Quick Birds and IKONOS images from Bushehr
harbor and Kish Island in Iran were used as case study. Figures
6 and 7 show the source input images with their manually
