Hu Xiangyun
experiments have been done for practical mapping from images.
Figure 4 shows a road extraction. There are two seed points at the end of the road segment. The crosses in (b) are the
max-correlation points. Some of them are errors. Part (c) shows the result after segmented curve fitting and optimization
by Hopfield neural network. The result indicates the effect of the algorithm.
Figure 5 is a semiautomatic extraction result of a road network in an aerial image of which scale is 1:25000, all
extracted central lines of road are fitted by cubic spline. Part (b) is a snapped vector window of the result.
(a) | | (b)
Figure 5. Extraction result of a road network
5 | CONCLUSIONS
The practice indicates that our algorithm and human-machine cooperation has good accuracy, steady output, high speed
and good interactivity and is much more effective than traditional manual digitizing. Our algorithm uses some
experiential parameters or thresholds, such as the resampling range (searching range of template correlation), error
restriction of curve fitting and weight coefficient of weight function of the Hopfield network. Face to practical
production of digital mapping, it is good as long as it accomplishes the task.
However, our model of road extraction is based on a "light ribbon like ' feature, there are still problems in extraction in
downtown area and color image. The template might be more complex and the optimization should integrate more
factors, such as texture and color information. That is the farther work.
REFERENCES
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Gruen A,Li H H., 1996. Linear feature extraction with LSB snakes from multiple images. In: International
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Gruen A, Li H H., 1995. Road Extraction from aerial and satellite images by dynamic programming. Journal of
photogrammetry and remote sensing, Vol 30,pp. 11-20.
Gruen A, Li H H., 1995. Semiautomatic road extraction as a model driven optimization procedure. Digital
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