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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
In fig 4, the AB, C> D and E are initial seed points, the bold
line above represents a road in RS image, the broken lines
represent the direction of template matching.
Choose the maximum correlation result as the only matching
result of the segment. If the only matching result is larger than a
given threshold, it’s an efficient matching result. Take several
maximum efficient matching results as adopted matching
results.
At the matching place of adopted matching results add seed
points automatically, these seed points accompany with the
initial seed points, composed the seed points of LSB-Snake
model.
After processing above, on the one hand, we compute the sum
of the number of corresponding templates of the efficient
matching results, take the template of maximum sum as the
width of the road; on the other hand, compute the frequency of
“bright ridge” and “dark ridge” template responding to the
adapted matching result respectively, educe the road’s attribute
of dark or bright by the higher frequency.
3. EXPERIMENTS
A program is developed to realize the method above using
Visual C++ language |ll] , a series of experiments is presented to
extract roads from IKONOS and QuickBird image, to compare
the result of road extraction by our method and LSB-Snake
model.
Fig. 5 uses IKONOS image, in this experiment, there almost
doesn’t interfered land object around the road. Input several a
few seed points as fig. 5a, the round points are inputted
manually, and the extraction result of LSB-Snake model is
presented, the extraction result of auto-initial-value LSB-Snake
model as fig. 5b, the rectangle points are added by self-adapt
template matching method.
a b
From this experiment we can see that, if the initial seed points
are not dense enough, the result of road extraction by
LSB-Snake model is incorrect, while the auto-initial-valued
LSB-Snake model can extract road correctly, it’s more robust
and automatic than LSB-Snake model.
Fig. 6 uses QuickBird image, in this experiment, there exist
heavy shade or shelter around of the road by trees and houses,
fig. 6a isthe result of road extraction by LSB-Snake model, fig.
6b is the result of road extraction by auto-initial-valued
LSB-Snake model.
b
Fig. 6 Comparing of Road Extraction Results
From this experiment we can see that, if the shade or shelter is
heavy, the extraction result would be incorrect as fig 6a
indicates, while the auto-initial-valued LSB-Snake model can
overcome the shelter and extracts road correctly, more powerful
in anti-jamming than LSB-Snake model.
4. CONCLUSIONS
Based on the research of LSB-Snake model, this paper put
forward an auto-initial-value LSB-Snake model using self-adapt
template matching method to extract road feature in high
resolution remote sensing image semi-automatically. IKONOS
and QuickBird image are taken for experiments, manually input
the same amount of initial seed points, comparing the extraction
result of the LSB-Snake model and auto-initial-valued
LSB-Snake model. Experiments indicate:
The auto-initial-valued LSB-Snake model can add seed points
automatically by self-adapt template matching. Given the same
amount of initial seed points, our method is more robust than
LSB-Snake model.
Needn’t manual input of the parameter, such as the width of
road, dark or bright, the auto-initial-valued LSB-Snake model is
more automatic than LSB-Snake model.
The auto-initial-valued LSB-Snake model can overcome the
shade or shelter of land objects such as building and trees, and
more powerful in anti-jamming than LSB-Snake model.
Fig. 5 Comparing of Road Extraction Results
While, because the condition of the road in High resolution
remote sensing image is complex, our method of road extraction